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D1.4- Final Project Management Report
Document Number D1.4
Status Final
Work Package WP 1
Deliverable Type Report
Date of Delivery 31/12/2017
Period Covered 1st July 2015 – 31st December 2017
Responsible Unit WIT
Contributors Diarmaid Brennan (WIT),Martin Tolan (WIT),
Teodora Sandra Buda (IBM), Olga Uryupina
(UNITN), Alberto Mozo (UPM), Marius Corici
(FOK), Angel Martin (VIC), Domenico Gallico
(IRT)
Keywords Project Management, Objectives, Results,
Achievements.
Dissemination level PU
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 2 of 51
Change History
Version Date Status Author (Unit) Description
0.1 30/11/2017 Working Martin Tolan (WIT)
Diarmaid Brennan (WIT)
TOC & Section responsible
0.2 05/12/2017 Working Martin Tolan (WIT)
Diarmaid Brennan (WIT)
Angel Martin (VIC)
TOC and sections refined
WP6 consolidated contribution
0.3 10/12/2017 Working Marius Corici (FRAUN) WP5 contribution
0.4 15/12/2017 Working Angel Martin (VIC)
Teodora Sandra Buda (IBM)
WP6 updated contribution
WP2 contribution
0.5 23/12/2017 Working Olga Uryupina (UNITN)
Domenico Gallico (IRT)
Teodora Sandra Buda (IBM)
Martin Tolan (WIT)
WP3 contribution
WP7 contribution
Updated WP2 contribution
0.6 30/12/2017 Working Martin Tolan (WIT)
Alberto Mozo (UPM)
Marius Corici (FRAUN)
WP4 consolidated contribution
WP5 updated contribution
0.7 30/12/2017 Working Martin Tolan (WIT) Review of all sections.
0.8 31/12/2017 Working Diarmaid Brennan (WIT) Review of all sections.
1.0 31/12/2017 Final Martin Tolan (WIT) Final version for release
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 3 of 51
Abstract
Keywords
The goal of this deliverable is to describe the objectives of the CogNet project as set out in the
original proposal and to capture the solutions that were synthesised addressing those objectives.
This document also captures some of the elements of the project management approach applied
that provided for a smooth running of the project including collaboration and communications
between work packages and risk identification and mitigation.
5G, Project Management, Collaboration, Risks, Integration, Testing, Validation, Demonstrators,
Infrastructures, Testbed.
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 4 of 51
Table of Contents
Change History .............................................................................................................................. 2
Abstract .......................................................................................................................................... 3
Keywords ........................................................................................................................................ 3
Table of Contents .......................................................................................................................... 4
1. Project Context and Objectives........................................................................................... 5
2. Main Scientific and Technical Results ................................................................................ 7
2.1. WP2 - Requirements and Architecture ......................................................................................... 7
2.1.1. Objectives considered by this work package....................................................................10
2.2. WP3 – Advanced Machine Learning for Data Filtering, Classification and Prediction ......11
2.2.1. Objectives considered by this work package....................................................................11
2.2.2. Achieved Results .....................................................................................................................12
2.3. WP4 – Network Resource Management ....................................................................................16
2.3.1. Objectives considered by this work package....................................................................16
2.4. WP5 – Network Security & Resilience.........................................................................................19
2.4.1. WP5 Objectives and Approach ............................................................................................20
2.4.2. Achievements of the work package ....................................................................................22
2.5. WP6 – Validation & Integration ...................................................................................................25
2.5.1. Objectives considered by this work package....................................................................30
3. Risks and mitigation actions ............................................................................................. 32
4. Potential Impacts ................................................................................................................ 35
4.1. Main Dissemination Activities .......................................................................................................36
4.2. Publications and events participation .........................................................................................36
4.3. Collaboration with other EU Groups and Projects....................................................................47
4.4. Exploitation of Project’s foreground ...........................................................................................48
5. Project Details ...................................................................................................................... 49
5.1. Meeting Metrics...............................................................................................................................50
6. References ............................................................................................................................ 51
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CogNet Version 1.0 Page 5 of 51
1. Project Context and Objectives
The goal of the CogNet project was to make a major contribution towards autonomic management
of telecoms network infrastructure through the use of available network data and applying
Machine Learning algorithms to yield insights, recognise events and conditions and respond
correctly to them. The project goal was to develop solutions that provides a higher and more
intelligent level of automated monitoring and management of networks and applications, improve
operational efficiencies and facilitate the requirements of 5G. The project conducted and exploited
leading research in the areas of data gathering, machine learning, data analytics and autonomic
network management. The ultimate objective was to enable the larger and more dynamic network
topologies necessary for the 5G networks, improve the end-user QoS, and to lower capital and
operational costs through improved efficiencies and the use of node, link and function
virtualisation. To realise this, and to create this value, the multi-stakeholder CogNet consortium
identified a number of key project objectives; each of which is associated with a distinct set of
innovations.
Objective 1: Research and develop a system of data collection from network nodes that involves
pre-processing data to allow the node classify the data it generates and identify the most important
and irregular data for submission to network management while filtering routine and regular data.
This is an important step in the development of scalable network management as it dramatically
reduces the scale of data required to be processed centrally.
Objective 2: While working on the principles of a self-organising network, research and develop,
within existing policy management frameworks, a system to allow network nodes to self-manage
based on their available data while escalating higher importance issues to central network
management.
Objective 3: Apply Machine Learning algorithms to develop a system of service demand prediction
and provisioning which allows the network to resize and resource itself, using virtualisation, to
serve predicted demand according to parameters such as location, time and specific service
demand from specific users or user groups. This is achieved while optimising performance and use
of available network and VM resources while minimising overall energy requirements and costs.
Objective 4: Apply Machine Learning algorithms to address network resilience issues. This includes
using Supervised ML to identify network errors, faults or conditions such as congestion at both a
network wide and a local level and automatically taking mitigating actions to minimise overall
impact.
Objective 5: Use anomaly detection algorithms to identify serious security issues such as
unauthorised intrusion or fraud and liaise with autonomic network management & policies to
formulate and take appropriate action.
Objective 6: Develop a number of demonstrable applications using real-world data gathered via
current 4G network nodes which demonstrate the core project innovations, and serve to highlight
the exploitation potential of CogNet. The applications will include tests to demonstrate the
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 6 of 51
potential improved performance and capacity that can be achieved by utilising the CogNet
algorithms over conventional approaches used in today’s Network Management Systems.
The project consists of seven work packages, each of which are summarised here:
WP1 is responsible for all project administrative, technical coordination, innovation
management, and quality management. WP1 has a series of complementary, yet dedicated,
tasks each of which focuses on a particular management aspect of the project. In addition
to administrative and technical project coordination, this WP deals with the management
of project innovations, including IPR, with the inclusion of a dedicated Innovation Manager
role – closely aligned with this activity is the exploitation and dissemination of project
results (managed by WP7). WP1 provides oversight on all project activities from the
perspectives mentioned above, as detailed in the WP description tables.
The overall goal of WP2 is to identify the project requirements, scenarios and use cases
which will drive all project technical activities. A set of initial scenarios will be developed by
the project team, and these will be used as the basis for our requirements, focusing on
business, stakeholder and technical aspects. Additionally, this work package will specify all
technical decisions and architectural viewpoints of the CogNet system.
WP3 will focus on the development and adaption of Machine Learning algorithms to filter,
classify and develop insights into the data that will be used to fulfil the use cases being
researched in WP4 and WP5.
WP4 and WP5 represent some the core applications of the CogNet project. WP4 will focus
on the application of Machine Learning to Resource Requirement Prediction and Efficiency
for Telecoms Networks with the particular application being the autonomic management
of resources for Network Function Virtualisation.
WP5 will focus on the application of Machine Learning to Security issues (including fraud
detection, intruder detection and subversion of machine purpose) and Network Resilience
(Error detection and correction, performance degradation and correction). This research
will also feed into applications in Autonomic network management.
The objective of WP6 is to integrate all developed software components and to carry out
test and validation of same. WP6 also focuses on integration of the core CogNet system
with complementary technologies with a particular focus on data visualisation, and on the
development of a number of exemplar demonstrators to highlight the value of the key
CogNet innovations.
Finally, WP7 is dedicated to the dissemination and exploitation of project results.
Dissemination and exploitation is a key element in the project. The objective is to reach out
to relevant communities of stakeholders through a comprehensive dissemination and
communication strategy. In parallel, the project outputs will be exploited through a series
of exploitation activities – as set out in the individual partner exploitation plans.
The CogNet project ran from July 2015 until December 2017 and successfully achieved all of its
proposed objectives.
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2. Main Scientific and Technical Results
The main scientific and technical results for the CogNet project is broken up on a work package
basis and captures the following for each work package:
Problem description including the State of the art.
Solutions detailing what innovations are included.
Overall project objectives met by the solutions.
2.1. WP2 - Requirements and Architecture
Figure 1 WP2 tasks’ composition, methodology and outputs.
CogNet WP2 entitled Requirements and Architecture is responsible for the following three tasks,
illustrated in Figure 1:
1. Task 2.1: Identify the Use Cases and Scenarios of the CogNet project that can illustrate the
impact of the advancements of network management in 5G in some real life scenarios.
2. Task 2.2: Model and design the technical and business requirements for the CogNet
project, covering the representative set of usage scenarios and arranged in a hierarchy
supported by a CogNet information model.
3. Task 2.3: Engineer the high-level architecture of the CogNet system as a harmonious set of
services, service components and configurations that meet the requirements in all
representative deployment domains.
Figure 1 illustrates with yellow the main outputs of WP2, which are detailed in the following
paragraphs.
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 8 of 51
Figure 2 Final set of CogNet challenges, use cases and scenarios.
The initial efforts in WP2 concentrated on identifying and defining a set of challenges, use cases
and scenarios and their requirements, related to the 5G network, specifically from a network
management perspective. Deliverable 2.1 introduced six use cases of CogNet based on the
challenges of the future 5G network management, such as network resource utilization, network
performance degradation, and energy efficiency. The use cases explored in this project were: (1)
Situational Context, presenting how the system will handle exceptional situations due to external
environmental conditions which cannot be directly detected within telecommunication systems.
(2) Just-in-time Services, referring to how cognitive network management techniques will enable
the reduction of creation and deployment time for network services in 5G. (3) User-Centric Services,
moving towards a richer and more complex service catalogue, with the capacity of tailoring services
to the particular user’s needs. (4) Optimized Services in Dynamic Environments: enabling the
network to be deployed, scaled and migrated with ease and speed unheard of in today’s networks,
specifically by relying on the virtualization of the network functions. (5) SLA Enforcement, handling
in an automated and efficient way the level of service guaranteed to a user or service by the
network operator. (6) Collaborative Resource Management, where both the network and the
applications at both endpoints exchange metadata about the network flows in order to improve
network conditions and user experience. Furthermore, Deliverable 2.1 introduced eleven initial
scenarios pivoting around the above use cases in order to facilitate more specific research
questions of high impact value in a real life situation. These were reduced to seven scenarios in
Deliverable 2.2. The scenarios range from large scale events prediction and urban mobility
awareness to massive multimedia content consumption. The challenges, use cases and scenarios
identified in CogNet are presented in Figure 2. Furthermore, their associated requirements were
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CogNet Version 1.0 Page 9 of 51
presented in Deliverable 2.2, which included an analysis of the functional, non-functional and the
business related requirements from the various viewpoints: (i) 5G scalability - increasing demands
for higher performance and better quality of user experience in the 5G network, the widespread
generation of big data via the 5G network, security and privacy concerns, data ownership; (ii) 5G
autonomic network management, and last but not least (iii) 5G sustainability. Moreover, Deliverable
2.2 illustrated how the proposed architecture can serve in delivering the CogNet final set of
scenarios by presenting the sequence diagrams of each scenario and thus the communication flow
along the components involved from the architecture.
Figure 3 CogNet Architecture Overview.
The CogNet project has developed solutions integrating machine learning and Software Networks
to provide a higher and more intelligent level of network management to ensure quality of service
(QoS), improve operational efficiencies and reduce operational expenditure of 5G networks. To
achieve this goal WP2 focused on engineering a high-level architecture of CogNet bringing a
cognitive solution to NFV/SDN management that aims to tackle the challenges in the area (Buda,
et al., 2016). The overview of the CogNet architecture is presented in Figure 3. Compared with
several related architectural frameworks that handle 5G network management, such as (Sanchez,
et al., 2015) (Jiang, Feng, & Qin, 2015) (Jeon, Corujo, & Aguiar, 2015), the proposed architecture is
enhanced by both batch and real-time machine learning solutions to enable much more flexible
and dynamic networks, which can scale horizontally or vertically to handle various 5G scenarios.
The CogNet architecture (Xu, et al., 2016) aims to complement the NFV reference architectural
framework of European Telecommunications Standards Institute (ETSI) (ETSI, 2012), in which
hardware resources are orchestrated and managed, with machine learning capabilities towards an
automated network management solution. The state and consumption records on the hardware
resources are gathered in real-time from multiple functional blocks constituting the layered
architecture. The collected records will be processed by the CogNet Smart Engine (CSE) or
Lightweight CSE (LCSE) periodically or in (near) real-time, to create insights from telecom data or
to recommend policies best matching the network management goals. Real-time analysis is one
of the core contributions of this work. Such a capability is crucial to 5G network management since
it aims to provide immediate response to changes. Furthermore, WP2 abstracted the underlying
CogNet Solution
Network Management Existing Solutions
NFV/SDN-based Environment
Data Collector
Policy Engine
DataStream
ScoresPolicies
Data Stream
DataStream
Real T
ime
Engin
e
Co
gN
et S
mart
En
gin
e
Cog
Net
Da
ta C
olle
cto
r
Co
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et
Po
licy E
ng
ine
Batc
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Engin
e
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 10 of 51
services supported by CogNet through the portfolio of CogNet services presented in Deliverable
2.2, which encapsulate the core work developed in CogNet into a set of services, such as location-
based services, and quality assurance services. Multiple services can be selected and integrated
based on the requirements of an operator and the availability of data. Data services are used to
import and process the data required by the machine learning modules. Machine learning services
provide the core predictive functionality and the planning services orchestrate the predictive
services for action recommendation and policy implementation. In addition, Deliverable 2.2
introduced the associated CogNet information model, which illustrates the core information
captured by the CogNet architecture. This clarifies the information expected to flow between the
CogNet architectural components and their corresponding interfaces.
Finally, the deliverables of WP2 served as a guideline to identify the research questions and their
solutions for the other work packages. Based on the final identified use cases and scenarios,
CogNet proposed candidate solutions on the supporting CogNet architecture to address them.
These solutions materialized into a set of associated demonstrators which are presented in WP6.
2.1.1. Objectives considered by this work package
The main objectives considered and met by the WP2 are:
Objective Coverage Description
Objective 1 The CogNet architecture has dedicated components for data collection and pre-
processing (such as filtering, cleaning, transformation). Furthermore, the
architecture supports the streaming of real-time and offline data that passes
through these components and if necessary through additional more
sophisticated pre-processing blocks such as feature extraction depending on
the requirements before being passed to the Batch or (Near) Real-time
Processing Engine for analysis. Moreover the Data services were designed to
handle the data gathering, preparation and dimensionality reduction aspects
related to this objective.
Objective 2 The Policy Engine within the CogNet architecture supports the recommendation
of actions to the MANO block based on the Machine Learning insights gathered
from the particular environment under analysis. Moreover, the Planning services
were designed to support the actions recommendations and policies
implementation.
Objective 3 The Machine learning services were designed to support core predictive
functionality. Moreover, most scenarios focus on the application of Machine
Learning algorithms to specific network management issues. For instance, the
Urban Mobility Awareness scenario targets this objective by utilizing a network
demand prediction model considering the users’ mobility in an urban region
and their associated patterns of utilization. In addition, Massive Multimedia
Content Consumption (corresponding to the Media SLA demo) focuses on
service demand prediction and provisioning. Moreover, the requirements were
formulated with additional considerations for Intelligence and associated
Machine Learning KPIs.
Objective 4 The Dense Urban Area scenario and associated demo, built following the
CogNet architecture specifically targets resilience issues and thus this objective
through deep-learning based anomaly detection.
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Objective 5 The Dense Urban Area scenario, Massive Multimedia Content Consumption
(corresponding to the Media SLA demo) and Detection and Reparation of
Network Threats have been formulated to consider the underlying performance
degradation detection and their corresponding actions, SLA compliance and
associated policies in case of SLA violation detection, and security threats
identification specifically targeting this objective. Moreover, the CogNet
architecture and information model supports this objective and associated
solution.
Objective 6 The high level architecture design and information model emerged from the
use cases and scenarios defined in Deliverable 2.1 and is leveraged further in
WP6 for the design of the common infrastructure. The architecture thus is the
underlying pillar supporting the demonstrators built in this project.
Table 2-1 Coverage of CogNet Objectives in WP2
2.2. WP3 – Advanced Machine Learning for Data Filtering,
Classification and Prediction
The prospective 5G networks will provide services to a myriad of diversified devices that are
constantly exchanging massive volumes of data according to their communication needs for a
plethora of various applications. To function smoothly without interrupting the provisioning of
their services, while being at the same time cost-efficient and secure, the networks will require
effective and highly responsive management techniques, adapted as much as possible to the
specific situation. Manual management of these networks by means of a set of rigid predefined
rules or policies does not scale. Therefore, the prospective 5G networks should be capable of highly
adaptive cognitive self-management.
In CogNet, we focus on a wide variety of use cases and scenarios to tackle challenges of the 5G
networks. In particular, we expect a sharp increase in the network consumption, both quantitatively
(large number of connected devices in the Internet of Things paradigm) and qualitatively (complex
typologies of users and devices in a network, different and constantly changing types of request).
To deal with this complexity, CogNet proposes dynamic solutions to network management based
on machine learning, implemented within the framework of CogNet Smart Engine. Given the variety
of addressed scenarios, the CogNet Smart Engine supports different types of machine learning
models. All these models have been optimized to operate in the Big Data setting, providing scalable
and robust processing of large amounts of data. Work Package 3 focuses on design and
implementation of algorithms within the CogNet Smart Engine as well as integration and
adaptation of off-the-shelf state-of-the-art machine learning modules. Below we discuss the WP3
objectives and accomplished results.
2.2.1. Objectives considered by this work package
This Work Package is responsible for studying, designing and implementing new machine
learning (ML) algorithms for network/cloud management. It was designed to follow four main
research lines:
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 12 of 51
Application of the latest result of the statistical learning theory, such as kernel methods
and structured output spaces for modelling the interaction between network nodes.
Domain adaptation methods for enabling the use of supervised techniques in the
network environment, where the high variability of data requires a high level of
abstraction such that automatic classifiers can effectively work on unseen input.
Unsupervised and semi-supervised approaches for helping the domain adaptation ability
of the classifiers (thus they can work in different conditions).
The use of the above methods for prediction tasks using time series.
In addition to the novel directions above, this WP was expected to take care of the application of
traditional and well assessed best practices of ML to the classification/regression/prediction tasks
in the 5G domains:
Processing the raw data, harvested from distinct sources, for transforming it in a suitable
format for ML algorithms.
Feature engineering and extraction from the raw data.
Setting the right parameters for the different ML algorithms, applying feature selection
techniques to reduce space dimensionality and thus improve efficiency, filtering
inconsistent (and so useless) training examples.
To determine the strength and weaknesses of the ML models, i.e., to ensure quality, each model
was supposed to be tested according to efficiency and accuracy. Finally, WP3 delivers the ML
models to WP4 and WP5, which further refine, specialize and adapt them for their test case.
While the output of WP3 contributed to all the project objectives listed in Section 1-Project
Context and Objectives above, it is particularly important for the successful achievement of
Objectives 1, 3 and 4.
2.2.2. Achieved Results
Work Package 3 is one of the central building blocks of the CogNet solution, with 7 partners
contributing with about 120 person months. This effort has been invested into creating a reliable
ML-based technology for 5G network management, achieving all the declared objectives. The main
output of WP3 is presented in detail in four deliverables (M8, M16, M22, M26). Deliverable D3.1
outlines the technology to be developed and the main issues to be addressed, Deliverables D3.2
and D3.3 release the prototypes and, finally, Deliverable D3.4 presents an extensive evaluation
report. Below we provide a brief summary of the developed solution.
2.2.2.1 CogNet Smart Engine
All the ML components developed within the work package form part of the CogNet Smart Engine
(CSE). The CSE, designed following the architectural specifications proposed within Work Package
2, delivers a principled solution to a wide variety of ML tasks arising within 5G scenarios. All the
WP3 partners have contributed to the design and implementation of the CogNet Smart Engine,
producing individual components or adapting state-of-the-art ML solutions to 5G tasks through
D1.4 - Final Project Management Report
CogNet Version 1.0 Page 13 of 51
extensive optimization and, in particular, parallelization to improve the CSE scalability and
robustness in the Big Data context.
In total, the CSE comprises 20 different components, addressing various ML tasks. They have been
extensively evaluated on several datasets, either common benchmarks or generated specifically
within the project (see Deliverable D3.4 for details). All the components run on the same
infrastructure and can communicate with each other through the CogNet infrastructure as
developed in WP6.
Table 2-2 presents CSE components from the deployment perspective, showing the 5G use cases
and scenarios relevant from D2.2. The modules that were selected to be included or used as a pre-
processing step in CogNet demos are highlighted with a light blue background.
Table 2-2 WP3 CSE components and their application in the 5G context.
Component Name
(Developer)
5G Application (Relevant WP)
CogNet Demo (If applicable)
WP deployed to
(Deliverable reported
in)
Spark IterFS (UPM) Feature selection for 5G Tasks (WP4)
This module was used as a pre-processing
step in the MMCC (Traffic Classification)
for feature selection.
WP3 (D3.2, D3.3)
PICS/PPICS (UPM) Feature selection for 5G Tasks (WP4)
These modules were used as a pre-
processing step in the MMCC (Traffic
Classification) for feature selection.
WP3 (D3.2, D3.3)
NetSpark (UNITN) Automatic feature engineering for 5G
tasks (WP4, WP5)
WP3 (D3.2, D3.3)
ML4MQ (Orange) SLOs breaches identification at service
level, Throughput prediction, SLA
enforcement (WP5)
WP3 (D3.2, D3.3)
Connected-cars-ml:
Mobility Pattern
Prediction (VICOM)
Mobility patterns for connected cars
(WP4)
This module is used in the Connected
Cars demo.
WP3 (D3.3), WP4 (D4.2,
D4.3, D4.4), WP6 (D6.1,
D6.2)
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Anomaly Detection
Engine - ADE (IBM)
Performance degradation detection
(WP5)
This module is used in the Dense Urban
Area demo.
WP3 (D3.3), WP5 (D5.3),
WP6 (D6.2)
NDP (IBM) Network demand prediction (WP4)
This module is used in the Urban Mobility
Awareness.
WP3 (D3.3) WP4 (D4.3)
NTC (IBM) Network traffic classification (WP4) WP3 (D3.3)
RCMR (IBM) Recurrent crowd mobility recognition for
network consumption analysis (WP4)
WP3 (D3.3)
PSCEG (UPM) Clustering of network metrics (WP4)
This module was used in the preliminary
steps to explore data clusters in MMCC
(Traffic Classification) and Noisy
Neighbours.
WP3 (D3.2)
ForwardEC (Orange) Prediction of network metrics (WP4) WP3 (D3.3)
FunCo (Orange) Network resource management (WP4,
WP5)
WP3 (D3.2, D3.3)
FunPrev (Orange) Forecasting anomalies in networks (WP5) WP3 (D3.3)
Distributed Application
Performance Optimizer
(VICOM)
Network resource management (WP4,
WP5)
This module was used in the preliminary
steps in MMCC.
WP3 (D3.2, D3.3)
SPARK-TK: Machine
Learning for Structural
Input (UNITN)
Explicit modelling of network
configurations and other structural data
(WP4)
This module is used as a preprocessing
step in the Large Scale Events.
WP3 (D3.2) WP4 (D4.2)
LSSVM-SP: Structured
Output Prediction
(UNITN)
Explicit modelling of network
configurations and other structural data
(WP4)
WP3 (D3.2, D3.3), WP5
(D5.3)
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ModelsDiff: Log-based
Behavioral Differencing
(Nokia)
Network performance evaluation (WP3) WP3 (D3.3)
TCDC (IBM) Model evaluation and selection (WP3) WP3 (D3.1)
Stream-Cluster (VICOM) Network resource management (WP4,
WP5)
WP3 (D3.2)
Stream-Regression
(VICOM)
Network resource management (WP4,
WP5)
WP3 (D3.2)
2.2.2.2 Scientific Output
WP3 was conceived as a more theoretical and research-oriented work package: within its context,
we have developed novel ML techniques and investigated possibilities for adapting state-of-the-
art ML solutions to 5G problems from a more general perspective. The output of WP3 has then
been further integrated into WP4 and WP5 for more empirical studies. In accordance with this view,
the work package has produced a considerable amount of academic dissemination: to ensure the
high quality of the CogNet technology, we have always stayed in touch with the research
community, paying close attention to the latest ML developments as well as presenting and
discussing our results. In particular, WP3 has produced the following:
40 conference papers, including several top-tier venues, winning 2 best paper awards,
2 journal papers and 2 invited journal papers in preparation,
1 book chapter and
4 invited talks.
These achievements are reported in more detail in WP7 deliverables.
One especially important WP3 achievement is the organization, in collaboration with WP5, of the
NetCla Traffic Classification Challenge, collocated with the ECML-PKDD 2016. The challenge,
providing an open source benchmark for traffic classification studies, has attained a considerable
attention of both academic and industrial communities, with 25 participants from all over the world
submitting their solutions. This competition has a two-fold impact on the 5G industry: first, it
presents a reliable setting for evaluating prospective technologies and second, with a wide variety
of high-quality ML solutions submitted by the participants, it provides insights on state of the art
in machine learning for 5G domains.
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2.3. WP4 – Network Resource Management
Work Package 4, named Network Resource Management, aimed to research and develop smart
real-time analytics techniques for optimizing the performance of Virtual Network Functions (VNFs)
in terms of energy efficiency, quality of service and resource elasticity by means of orchestration
mechanisms. More specifically, WP4 addressed the limits of existing analytics and machine learning
techniques for software network environments.
WP4 goals and activities have been driven by CogNet Objective 3 that proposed the application of
Machine Learning algorithms to develop a system of autonomous service demand prediction and
provisioning according to parameters such as location, time and specific service demand from
specific users or user groups. Additionally in the context of this objective, WP4 has investigated
how to optimize the performance of available network and infrastructure resources jointly with the
minimization of the overall energy requirements and costs.
To this end, four tasks were initially proposed: 1) Task 4.1 Data gathering and pre-processing, 2)
Task 4.2 Techniques for prediction in NFV scenarios, 3) Task 4.3 Smart self-managed NFV
ecosystem for optimal elasticity and energy efficiency, 4) Task 4.4 Online network layer traffic
classification and prediction, and Task 4.5 Integration and correlation of network data and NFVI
events.
Given that early identification of use cases and scenarios was provided by work package 2, an
alternative organization based on use cases and scenarios was adopted in WP4 activities instead
of the normal task oriented approach. The goal of this new approach was to maximize the number
of research topics under exploration and to foster rapid and flexible collaborations among small
groups of partners. Therefore, WP4 activities were carried out in parallel and following six different
research avenues for different use cases and scenarios. In the following subsection we show the
specific objectives considered by this work package, classified based on the chosen use cases and
scenarios.
2.3.1. Objectives considered by this work package
2.3.1.1 Optimized Services in Dynamic Environments (Use Case)
With the advent of Network Function Virtualization (NFV) and Software Defined Networking (SDN),
Network Functions (NF) will no longer be tightly coupled with the hardware they are running on.
This flexibility entails certain challenges when it comes to managing the infrastructure resources.
Among these, a recurring issue is the one known as the “Noisy Neighbour”. This problem arises
when various virtual machines compete for the same physical resources, causing performance
degradation.
In this work we have built a controlled cloud environment to reproduce the “Noisy Neighbour”
effect in order to collect a sufficient set of labelled examples for training supervised machine
learning models that can detect this issue. Initially, we designed and trained several classifiers based
on traditional state-of-the-art machine learning techniques such as random forest and support
vector machines obtaining decent accuracy results (around 0.95). After that, and in order to
increase the accuracy of the models, we proposed more complex models based on deep
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convolutional neural networks. As expected, when these deep models were trained in a big data
regime (i.e. using hundreds of thousands of labelled examples), the accuracy levels improved
significantly when compared to traditional ones even in the presence of complex scenarios. In
addition, we trained a regression model to predict high level KPIs that are difficult to compute
within the cloud infrastructure such as the jitter values that an end-user can be experiencing during
the reception of a multimedia stream. After testing several machine learning models, the ones
based on fully connected deep neural networks were revealed as being more accurate.
Preliminary research results of this work were presented in ESANN-17 conference and
complementary results (to appear in 2018) were submitted to Plos ONE journal (ranked in the first
quartile of the prestigious ISI/JCR index). Finally, we plan to submit an additional publication
containing the final results of the research done in this use case to a top-tier journal specialized in
the cloud and telecom domains.
2.3.1.2 Traffic Classification (Use Case)
The use of application-level encryption is becoming more and more prevalent in today’s Internet,
making it difficult for network service providers to characterize the traffic that traverses their
infrastructure. In addition, privacy issues preclude them from inspecting the packet payload (i.e.
application data) for classification purposes. Therefore, new methods for traffic classification and
characterization are being sought by network service providers. Under the hypothesis that
adequate mappings can be found using traffic features extracted from the transport and network
layers and below, we have investigated the application of machine learning models to help network
managers in the described scenario.
We have built a controlled environment entitled Mouseworld that generates realistic network traffic
data and can label automatically big amounts of traffic flows into pre-configured traffic categories.
Using these labelled traffic datasets we have trained traditional and deep classifiers to generate
accurate traffic characterizations (in the range of 0.85 and 0.95 of accuracy). Two different models,
forensic and real-time have been designed, trained and tested. Firstly, forensic models were
produced using random forest and deep fully connected artificial neural networks. Secondly, we
designed real-time models that can produce an accurate classification even when a small number
of packets have been received (e.g. 5-10 packets). These models were based on deep convolutional
neural networks and utilized as input time series of the features of a flow jointly with context values
obtained from concurrent flows. The results obtained at the end of this work confirm that the
researched techniques outperform state-of-the-art deep packet inspection systems with the
advantage of not needing to inspect application data.
Having recently finalized our last experiments, we plan to submit the research outcomes of this
work to a top-tier journal specialized in the telecom domain.
2.3.1.3 SLA Enforcement (Use Case)
SLA (service Level Agreement) refers to the level of service guaranteed (often through contract) to
a user or service by the network operator. With many modern IT services requiring different levels
of guaranteed bandwidth, latency and priority over other traffic, SLAs have become more important
and more differentiated depending on the nature of the service. For example, some types of
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services may need to take priority if there is contention for resources such as emergency service
communications, and in other SLA and situations, security may be the critical factor, so special
authentication of the sender and receiver, encryption and or selective routing of data through the
network may be a key issue that is addressed through the SLA.
The activities in this work target to set up an autonomic SLA management relying on Machine
Learning techniques to predict SLOs (Service Level Objectives) breaches. The use case chosen to
deploy the cognitive SLA enforcement was a streaming service running on SDN and NFV
infrastructure, in which a testbed was setup based on real cloud infrastructure where a streaming
service was running.
For the identification of the SLO breaches we focused on two types of Artificial Neural Networks,
namely Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN). In particular
we used a derivative of RNN called Long Short Term Memory (LSTM) for evaluation and comparison
purpose.
2.3.1.4 Network Demand Prediction (Scenario)
Prediction of demand in a network is an important part of autonomic network management. If a
5G operator can reliably forecast the demand in an area resource allocation can be done more
effectively by changing policies dynamically in CogNet infrastructure. This will lead to reduced
operation cost for operators and increased user satisfaction. A new approach for increasing the
network demand prediction accuracy is using the functional regions of a city. Functional areas in
dense urban areas change dynamically creating network demands that are dependent on both
location and time. Improvements in big data storage and processing capabilities enable us to build
location based prediction and forecasting models that take dynamic functionality of a region into
account.
The network demand prediction (NDP) module researched and developed in WP4 forecasts the
median throughput in a dense urban area for a flexible time unit. Forecast of network demand aids
a 5G network operator in planning for the changes in user demand. In addition, this module can
help service providers in identifying potential bottlenecks in their service ahead of time to achieve
higher operational performance.
The research results of work have been accepted to be presented in PAKDD-18 conference and are
under revision in the IEEE Transactions on Knowledge and Data Engineering.
2.3.1.5 Large Scale Events (Scenario)
This scenario employed machine learning to analyse traffic demand patterns based on network-
external evidence of social behaviour, extracted from social media and streams. The obtained
contributions evolve around applying machine learning algorithms for the network service demand
prediction, thus targeting the Objective 3 of the CogNet project.
Within this scenario, we have developed novel algorithms and models for different aspects of the
demand prediction problem. Thus, we have first created a prototype, publicly released within
Deliverable D4.2, for clustering Twitter data with the purpose of further events extraction. We have
then conducted exploratory analysis, correlating the mobile network consumption statistics
provided by TruConnect LLC and the Twitter/Foursquare data, contributing the respective software
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to Deliverable D4.3. Finally, we developed a machine learning model for traffic prediction
combining social media and historical network load data (see D4.4 for details).
The scientific output of this work has been presented at top-tier conferences such as CIKM
2015/2016, NAACL 2016, ACL 2017, EACL 2017, ECML-PKDD 2017, and in Journal of Natural
Language Engineering (to appear in 2018).
2.3.1.6 Connected Cars (Scenario)
In 5G systems there is a need to discover new wireless technologies between the handset and the
base stations that can handle very high-speed transmissions. Recently, antennas communicating
in Terahertz-band to achieve data rate that is close to Gbps have been proposed for small cells
with small coverage areas. The considered scenario, entitled Connected Cars, investigated how to
dynamically adapt mirrors to optimize 5G coverage in small cells and in particular researched
machine learning based methods for estimating the optimal orientation for each reflecting mirror
installed in a 5G small cell covered by one antenna based on the location of nodes (vehicles in this
case).
The main achievements and outcomes of the Connected Cars scenario are the following:
A system for collecting and pre-processing floating car data was developed.
A self-organised network management system was developed, which employs a policy
manager to broadcast new network configurations.
Two machine learning algorithms were developed. First, a Genetic Algorithm, and then a
convolutional neural network to obtain a faster online computation. These algorithms
were integrated into the CogNet’s Common Infrastructure.
The module developed in WP4 is part of a demonstrator that predicts the future vehicle
mobility pattern and mitigates the effect of vehicle traffic congestions reconfiguring the
network resources.
A demonstrator was developed, putting together the module developed in WP4, and the
mobility pattern prediction module developed in WP3. This demonstrator was integrated
in the Common Infrastructure developed in WP6. A floating car data dataset was
generated using a simulator of urban mobility (SUMO).
The research results of this work have been submitted and published in top-tier journals ranked in
the first and second quartiles (Q1, Q2) in the prestigious ISI/JCR index such us Journal of Real-Time
Image Processing, IEEE Transactions on Mobile Computing and IEEE Transactions on Broadcasting.
2.4. WP5 – Network Security & Resilience
WP5 is the work package handling the development of extensions of the management plane of
software networks with added value machine learning mechanisms enabling the preparation and
the prophylaxis for exceptional events.
During the project, the software network environment, initially based on the NFV and SDN
technologies as standardized by ETSI and by ONF evolved towards more diversified environments
while at the same time, a scope reduction was observed.
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Seen from the perspective of the end of the project, the software networks on top of cloud
infrastructures with or without NFV are becoming a norm in network developments and
deployments. From this perspective, WP5 mechanisms of resilience and security used for the
software networks and managed by the machine learning mechanisms are still very new, reaching
initial prototyping and products. Because of this, the results of WP5 are becoming essential for the
immediate next stage of development of software networks. This is underlined by the interest of
the operators and of the vendors in the results of the project as well as in the continuation of the
research and innovation activities towards other projects with higher TRL, closer to production.
Furthermore, SDN passed the expectancy gap and is becoming now an important constituent
technology of the 5G networks as well as of the dynamic data center networking solutions. As a
result, SDN solutions are currently maturing in the new generation of software products.
Security and resilience are usually features which come after the initial functional components of
the products are developed. Because of this, the results of WP5 are currently highly relevant not
only from the perspective of how machine learning can help with the management, but also from
the perspective of how remote decisions could be taken based on active system information and
how mitigation actions can be transmitted in the highly distributed software network environment.
2.4.1. WP5 Objectives and Approach
Concentrating on the security and the resilience of the software network functions, WP5 had
provided different objectives, which albeit being equally represented within the description of work
and added in tabular form underneath, evolved during the project and changed proportions mainly
due to the interest of the industry in specific areas.
WP5 aimed at reaching an appropriate set of security mechanisms based on the machine learning
addressing the dynamic network function environment of the software networks on top of cloud
(Objective 1). Although being a single objective, due to the very large interest of the industry the
project resulted into three distinctive prototypes each considering a specific angle of security. Even
with this, there still a very large potential to extend these prototypes and to further apply them
towards real-life deployments, especially because of the lack of proper security mechanisms still
noticeable in the cloud deployments.
For the end-to-end reliability of the network (Objective 2) a more pragmatic approach was taken,
specifically concentrating on the SLA maintenance for the specific services as seen from the
perspective of the subscribers. This approach is aligned with the NFV approach towards software
services where the management of the system has to maintain the SLA towards the subscribers
while adapting in an automatic and transparent way.
For the high availability (Objective 3) the approach taken was to integrate anomaly detection
mechanisms together with the high availability mechanisms of the software networks in such a way
as to be able to predict failures and reduction of performance and to adapt the system dynamically.
A final objective of the work package (Objective 4) aimed at providing an integration between the
machine learning mechanisms and the network management mechanisms for specific
deployments of specific networks, thus representing the convergence point between the
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developments coming from WP3 with the specific security and resilience mechanisms of the
network.
Objective Coverage Description
Objective 1
Large scale distributed security mechanisms addressing dynamic network
functions deployments, combining the information available at virtual network
functions level with administrative information and with the virtual network
fabric. WP5 addressed these items by investigating and developing a
distributed analytics framework for prophylactic preparation against various
security threats.
Objective 2
Machine Learning for dynamic network functions placement from the
perspective of providing a reliable end-to-end network service including the
provisioning of a distributed mechanism for ensuring high availability of the
functional processing, though this providing the means for automated service
administration.
Objective 3
Machine Learning and Real-time analytics for dynamic establishment and
adaptation of the network towards the momentary resilience requirements
ensuring the high availability of the network connections between dynamic and
elastic scaling components.
Objective 4:
Integration and harmonization of the end-to-end reliability mechanism by
integration of the local analytics (i.e. in each of the network tenants), end-to-
end service analytics according to the service specific device usage patterns
through this providing a predictable and replicable end-to-end reliability
mechanism
The WP5 work was split into three distinctive phases, each enabling the maturation of the
developed technologies throughout the project. Each of the phases was reflected in the form of a
deliverable as well as when the case in the form of prototypes, implementations and evaluations,
according to the initially proposed work plan:
1) Initial conceptualization – as reflected in D5.1, the main goal was to provide the initial
design of an integrated SDN/NFV ecosystem with machine learning algorithms through
this providing the means for a more robust network ecosystem for security and reliability
compared to the existing systems, which do not include the machine learning. The work in
this phase was aligned and influenced the architectural developments of WP2. As proposed
by the description of work, this deliverable achieved the initial system design from the
perspective of integrating machine learning functionalities within the SDN/NFV ecosystem
in the precise use cases that are addressed in WP5. The deliverable followed a holistic
overview of all the possible issues for security, resilience and long duration services aiming
to provide a pallet of possible options where machine learning is supposed to provide a
better performance than the current solutions.
In this initial conceptualization phase a large number of mechanisms for a very large
number of security and resilience problems were proposed with the aim of covering the
specific technologies and being able to offer towards the interesting parties different
directions of development.
2) First phase and second phase of implementation – as reflected in the D5.2 and D5.3 a set
of prototypes were implemented following the initial conceptualization phase. Albeit a very
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large number of security and resilience scenarios may have been considered within the
project, only the most significant ones from the perspective of the partners, especially the
operator as well as the third party industry customers of the work package members, were
developed and assessed. This allowed for the concentration of the resources towards
significant results with larger impact as well as towards underlining where the machine
learning is producing major advantages compared to the other policy based only solutions.
Although the prototypes were not specifically designed to be integrated into the common
infrastructure of CogNet an initial alignment with the WP6 activities as the integration
process was followed.
Furthermore, as WP5 mainly concentrated on the development of end-to-end prototypes
for showcasing the advantages brought by machine learning for network management, a
dual approach was taken:
i) the adoption of simple existing machine learning prototypes, through this bringing
the first understanding of machine learning techniques to partners specialized in
network management
ii) adoption of WP3 algorithms and adaptations from WP4 of these algorithms
enabling the machine learning specialized partners to understand and to adapt
their developments to the needs of network management.
Two separate implementation phases were considered in order to be able to have a middle
check-point on the developments as well as a comprehensive report on them, with this
minimizing the risk of having a reduced number of results within the work package.
3) The evaluation phase – in the evaluation phase (as reflected in D5.4) the prototypes were
measured and assessed towards underlining the key added value of the machine learning
techniques within the network management for network security and resilience. As
expected in this phase, some additional developments were needed within the prototypes
to be able to properly respond to the evaluation tests and requirements, adaptations which
could not be visible before the evaluation campaigns are started.
The approach taken enabled the proper integration of the specialists from two very distinctive
domains: network management and machine learning to the direction of security and resilience.
From this perspective, the work package achieved its goal and further developed research
relationships which will enable the further development of the prototypes towards trials as well as
the further development of the research and innovation area of network management for software
networks.
2.4.2. Achievements of the work package
The work package achieved its goals specifically by the development of a comprehensive approach
towards the security and resilience within the software network environments and by implementing
and evaluating selected testbeds which evolved during the projects towards the most interesting
directions of research in the domain.
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The testbeds showcase the advantages of machine learning for network management in the
directions of subscriber communication security, network security and network reliability and
performance. The list of the testbeds and components described in this deliverable is illustrated in
Figure 4.
Figure 4 – Testbeds of WP5
The end-to-end processing is split into three parts: monitoring, detection and actuation. For some
of the added value management features included in WP5, the same machine learning or the same
actuation is used. This is properly marked through the deliverables.
The following table introduces shortly the description of the testbeds and the value added
demonstrated using the machine learning techniques.
Testbeds Description and added value summary
Distributed
Security
Enablement
The distributed security enablement testbed, albeit addressing a firewall at the
entry of the network has a further actuation implementation of security zones
using SFC based routing, showcasing additionally to the large benefits of using
machine learning for detection of attacks the benefit of dynamic networking
solutions within the cloud infrastructures which allow with a minimal functionality
addition to create different security zones.
This is the main feature of interest of private network administrators which are
still trying to discover how their current security zones are provided by the cloud
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infrastructures, one of the main limiting factors of adopting cloud infrastructures
on a very large scale in enterprises.
Honeynet The honeynet testbed considered a classic approach to security, addressing mainly
the functionality of a firewall which is placed at the entry of the network and is able
to detect different attacks.
The provided evaluation results prove that the implementation of machine
learning based firewalls is feasible for a carrier-grade network and that it should
be considered in the next generation of products.
With this evaluation, operators are able to transmit to the vendors of software
firewall components the message that firewalls could be improved by more
dynamic data processing mechanisms and through this to reduce the final cost of
such components.
NFV
Security
Anomaly
Detection
The security anomaly detection testbed made a further step into the direction of
innovative features for security and to deploy a distributed security solution
directly on the forwarding plane of the cloud, through this liberating the firewall
functionality for the initial location into the infrastructure and enabling its
placement between any two connected virtual machines.
With this, security can be dynamically placed within the cloud environment and
could address in a distributed manner (i.e. gradually) different types of attacks.
Instead of concentrating on the placement of the network functions in different
network locations to implement the security zones, the project proved that it is
possible to place the security functionality at data path level and to use machine
learning techniques to address the detection of only specific attacks in a specific
location represents a new approach and new means to implement security in the
cloud/NFV environment using SDN mechanisms.
Resilience
testbed
The performance degradation testbed proved that machine learning represents a
cost-effective means to determine when the performance of a network function is
degrading and through this to predict the performance degradation of the
network service. The proposed mechanism also covers abnormal behaviour (not
only watchdog functionality) and proved that the anomalies can be detected and
mitigated in due time before their effect is noticed by the users of the service. This
represents a new approach to high availability where the specific machine learning
helps to solve the problem of component functionality degradation on the long
term.
Media SLA The Media SLA testbed addresses the performance degradation from the
perspective of the SLA of the subscriber, proving that the same anomaly detection
mechanisms should be used for the detection of the possible SLA degradation and
to mitigate in real time such situations.
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Added to this, an initial consideration of the development of an “Experience” framework for
network management was considered, where the machine learning will play a major role to
accumulate the proper insight for the more adequate network management decisions, based on
the proper interpretation of past situations and past action results. Albeit the “Experience”
framework remained only at the conceptual level enabling only the uniformity of the evaluations
of the testbeds towards underlining the machine learning added values, it provides a next step on
how the network management should be considered in highly dynamic and complex environments.
All the testbeds profited on the flexibility provided by the underlying cloud substrate of the
SDN/NFV environment and are using the new provided mitigation mechanisms (e.g. re-routing,
scaling, rebooting of network functions, etc.) to modify the network and the processing of the
specific services according to the decisions taken by the machine learning algorithms. With the
evaluation of the testbeds we have proven that machine learning techniques are feasible to be
used as advanced means for the network management decisions in real-time as needed to be able
to properly use the mitigation mechanisms proposed.
Furthermore, the proposed mechanisms were tested with local analytics, where the machine
learning mechanisms were placed in close proximity to the network functions themselves. With
this, a first evaluation of the algorithms was obtained providing to the reader the means to
understand that the specific features are highly beneficial when placed locally into the network.
However, this may be too complex as costs for real-life network deployments, as the distribution
of such functionality would require large support from the analytics functionality providers. For
assessing a more effective solution towards real-life deployments, most of the testbed algorithms
will be also deployed in the framework of WP6 as part of the common infrastructure, which from a
testbed perspective represents the centralized approach towards the network management. With
this, a second evaluation of the mechanisms is provided as well as the means to integrate them
into a single machine learning network management solution.
Since the start of the project, a very large momentum was seen for the machine learning usage in
performance and security management. From this perspective, WP5 managed to provide
innovation just in time for the needs of the software components providers, cloud providers and
operators to be able to assess in an initial form the benefits of machine learning in network
management and to make their decisions and roadmaps on the further integration towards the
later product integration.
Considering that WP5 has reached its major objectives, that the proposed testbeds showcase a
large number of performance and security related network management directions, all applied to
cloud/NFV infrastructures and considering the research and the development community interest
into the developed solutions, we consider WP5 as being successful, albeit being only an initial step
into the integration of machine learning,
2.5. WP6 – Validation & Integration
This work package has carried out activities pivoting around the following technical results:
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Demonstrators
The final goal of WP6 is to provide evidences of the successful results and benefits of using CogNet
results to specific Network Management problems present in 5G networks. To capture all the
expected features from CogNet, WP6 has implemented a set of different demonstrators which
meet the requirements and specific 5G challenges, explored in WP4 and WP5. The demonstrators,
selected from real business plans from partners, generate or use representative datasets to meet
target 5G challenges.
The WP2 established a set of target scenarios accompanied with intrinsic 5G challenges. The
scenarios have been formulated for showing real impact of cognitive network management. Thus,
these scenarios bring specific research questions and illustrate a significant impact in a real-life
context. Hence, the demonstrators are defined on top specific scenarios and named employing the
scenario they target.
The demonstrators and their setup are described in “D63 - Final release of the integrated platform
and performance reports”. The next figure depicts the current WP6 demonstrators of CogNet and
the different partners involved in each of them:
Figure 5 Demonstrators and participants around common scenarios
The network management situations supported by the tools applied on the demos are:
Demo
Network
Management
Domain
Short Description
Follow the Sun Self-healing The tools are able to identify virtual machines
performance degradation when being hosted by a
common physical machine, producing degradation
on the co-located service.
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MMCC - Real
Media SLA
Self-healing The tools are able to identify SLA breaches and to
re-scale/reroute VNFs the traffic accordingly to
guarantee a QoS.
MMCC – Traffic
Classification
Self-configuration The tools are able to classify the type of service of
each data flow without inspection of the payload or
the specific ports.
Dense Urban Area Self-healing The tools are able to identify performance
degradation situations on NFVs.
Detection and
Reparation of
Network Threats
Self-healing The tools are able to identify threats and apply new
rules to put threat’s origins in quarantine by NFV
setup.
Connected Cars Self-optimization The tools forecast the cars’ distribution on cells and
apply antenna setup to improve the signal coverage.
Urban Mobility
Awareness
Self-optimization The tools forecast the density of users in a city based
on data aggregation of network utilization and
social networks activity.
Table 2-3 Short Description of Demonstrators
Datasets
The employed Machine Leaning algorithms in the different demonstrators have been tailored to
process specific features from different datasets. The datasets have been extracted from a
representative and realistic setup or retrieved from real connectivity records. In the case of the geo-
binned dataset from WeFi1 , it includes network performance statistics from people surfing on the
Internet in the Manhattan area along 6 months. The datasets and the applicable licensing terms
are described in “D63 - Final release of the integrated platform and performance reports” and “D64
- Final evaluation and impact assessment results”.
Common Infrastructure
A major result of CogNet is the Common Infrastructure. A set of scripts and Ansible playbooks
which allows any network manager to deploy the full CogNet infrastructure ready to capture data,
analyse specific key features and provide actuation suggestion according to the identified or
predicted networking issues and the defined actuation. To host such shared WP6 testbed for all
the demonstrators, we decided to use a commercial Rackspace2 cloud infrastructure, based on
OpenStack technology. This decision aims to guarantee scalability and get independence from
specific setups of the facilities from the partners. The APIs and the set of instructions for its
deployment is being declared initially in “D62 - First release of the integrated platform and
performance reports” and its final specification in “D63 - Final release of the integrated platform
and performance reports”.
Furthermore, to check the flexibility to deploy the common infrastructure on top of different
facilities from different partners, the common infrastructure has been adapted to decouple from
Rackspace technology and APIs, and to enable private network addressing. Thus, the common
1 https://wefi.com/ 2 https://www.rackspace.com/
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demonstrator infrastructure can be cast over different virtualized infrastructures where different
use cases and challenges are addressed. The set of instructions for its deployment is being declared
in “D63 - Final release of the integrated platform and performance reports”.
This common infrastructure brings the following objectives: replicability, with a solution which can
be instantiated and the same experiment should be executed over different platforms/test-beds;
integration, shipping a common set of elements, dataflow, SW stack and APIs to be considered and
adopted; and pedagogical, providing a best-practice reference platform for developing cognitive
management solutions with machine learning.
Policy Engine
CogNet project provides the ability to self-heal in reconfigurable dynamic networks by use of policy
based network management actuation for correction and prevention, and for the reconfiguration
of these policies based on the updated knowledge of the Machine Learning about the network
being monitored.
The Policy Engine evaluates the Machine Learning outputs to identify if any violations have
occurred. This evaluation is based solely on the policy document that is directly associated with the
Machine Learning algorithm being executed. If the Machine Learning detects that a particular
situation has occurred the corrective action(s) as specified within the associated policy is deemed
the most appropriate actions to be executed.
From a high level perspective, the Policy Engine has a Kafka interface which takes the pushed JSON
events (with a SUPA ECA document structure), checks a condition on an instance of a policy and
triggers an action message if the condition is satisfied.
Figure 6 High level Policy Engine workflow
SUPA ECA policies will define the behaviour of the Policy Instance for each policy that will be
triggered for each demonstrator application. That programmed actions and behaviours from Policy
Instance have several parameters that can be static or dynamic.
Integration and Testing
Furthermore, in order to get a consistent integration, evaluation and interoperability of the
developed tools in WP6 based on Machine Learning techniques, we have defined a procedure for
all the demonstrators on top of the common infrastructure. To this end, an encapsulation
mechanism has been defined to turn the Machine Learning systems and environments into
modules to be used in the continuous integration and testing system, Jenkins.
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Figure 7 [L]CSE Integration and Validation procedure
Performance and Validation Reports
The validation is based on scenarios bringing metrics that can be evaluated to conclude
performance reports. This validation is performed on top of a demonstrator that conducts the
integration and validation activities. This way, the system employed to process signals and data is
common while the monitored network and the services and traffic issues in place are specific to
the demonstrators. This ensures a uniform mechanism for the integration and validation activities
across all the demonstrators. Going further, a significant result is the work done to make the
CogNet Common Infrastructure generic to be deployed on top infrastructures based other cloud
technologies and on private setups.
Summary
The main results from WP6 are compiled in the next table.
Topic Technical Result
Common
Infrastructure
Deployment documented to be replicated in another private or public
OpenStack-based infrastructure. This way the Common Infrastructure can be
instantiated as a commodity in private setups.
Policy Engine The Policy Engine runs SUPA ECA policies with actions that can include
parameters obtained in a direct way from the metadata or retrieved from the
connection to the network topology.
Policy Engine
Monitor
The Policy Engine includes an activity monitor to track the inputs from the
[L]CSE and the eventually triggered actions, being expanded to be used as a
record for testing and validation purposes.
Policy
Actuation
Verbs
A set of actuation verbs and the typical required parameters have been defined
and agreed to cover all the possible actions considered in the different demos.
Docker
Monitor
The different ML algorithms encapsulated and hosted in a Docker Machine are
monitored by Google cAdvisor to track resource usage and performance
characteristics of their running containers.
Demos
Integration
All the demos are integrated with the Common Infrastructure and deploying
them automatically by means of a Jenkins job.
Demos
Testing
All the demos are reporting performance and evaluation tests obtained in the
deployment on the common infrastructure by means of a Jenkins job.
Machine Learning
Common Infrastructure
Jenkins
Testing dataset to be
replayed
Docker Encapsulation &
Kafka/InfluxDB connection Demo logs sent by email
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Demos
Actuation
All the demos apply the action to the monitored network, modifying the network
behaviour, or push the action to a dashboard system to provide awareness to
the network manager of an identified or forecasted issue.
GitHub GitHub includes:
- the final design of the common infrastructure for deploying on top of a public
Rackspace infrastructure and on top of a private OpenStack infrastructure,
- the policy engine for dynamic actions,
- a tutorial to deploy a CSE by encapsulating the ML algorithms in a Docker
and connecting appropriately to Kafka queues with compliant SUPA ECA
policies and messages.
- a tutorial to inject data to the Inbound API and to consume the Outbound
API of CogNet Common Infrastructure and generate a log report for validation
purposes
- licensing terms for all the demos
Table 2-4 Summary of technical results
2.5.1. Objectives considered by this work package
The main objectives considered and meet by the WP6 are:
Objective Coverage Description
Objective 1 The implemented common infrastructure includes components for data
collection from network nodes by means of the Inbound API based on Monasca
messaging.
In order to classify and identify relevant features from traffic and network
metrics in a scalable manner, the common infrastructure provides a Docker-
based mechanism to integrate and deploy Machine Learning techniques and
running environment into scalable cloud infrastructures.
Furthermore, the common infrastructure includes two processing modes, Kafka-
based live data flows for real-time processing and InfluxDB-based on-demand
access for batch and training processing.
Objective 2 The policy engine, as a major component of the common infrastructure,
provides policy management and eventually escalates required actions
according to SUPA ECA defined policies.
Moreover, the policies and new data can be dynamically updated by means of
a Kafka bus. Furthermore, the SUPA ECA syntax is flexible enough to define
dynamic parameters.
Objective 3 The different demonstrators from WP6 are intrinsically related to the application
of Machine Learning algorithms to specific network management issues. Some
of them such as Massive Multimedia Content Consumption- Media SLA meets
service demand prediction and provisioning.
Objective 4 The different demonstrators from WP6 are intrinsically related to the application
of Machine Learning algorithms to specific network management issues. Some
of them such as Follow the Sun, Dense Urban Area and Urban Mobility
Awareness target to identify network faults or degradation or conditions such
as congestion at both a network wide and a local level.
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Objective 5 The different demonstrators from WP6 are intrinsically related to the application
of Machine Learning algorithms to specific network management issues. Some
of them such as Massive Multimedia Content Consumption- Traffic
Classification and Detection and Reparation of Network Threats explore and
address security issues applying policies to shield from them.
Objective 6 The employed Machine Leaning algorithms in the different demonstrators have
been tailored to process specific features from different datasets. The datasets
have been extracted from a representative and realistic setup or retrieved from
real connectivity records. In the case of the geo-binned dataset from WeFi3 , it
includes network performance statistics from people surfing on the Internet in
the Manhattan area along 6 months.
Table 2-5 Coverage of CogNet Objectives in WP6
3 https://wefi.com/
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3. Risks and mitigation actions
During the course of the CogNet project it was vital that any risks were identified as early as
possible and that mitigation actions were put in place in order to address the risks and minimise
any impact on the project. At the end of each quarter the work package leaders submitted a report
that contained the progress of the work package for the last quarter along with the plans for the
following quarter. This report also included any risks that the work packages may have identified.
These were all collated and discussed during the work package leaders phone conference (occurred
every Thursday) and were tracked throughout the life span of the risk. All of the reports are available
and a sample of the risk identified are captured here in Table 6.
Risk No. Short Description WP(s)
affected
Contingency Owner
1 Migration of demos to
common infrastructure is
a large unknown in terms
of effort
WP6 Identify potential
bottlenecks as early as
possible and contact
coordinator re additional
resources
Vicomtech
2 Use Case has not obtained
a Proof of Concept
showing some
improvement
WP4,
WP5
Re-examine the scope of
the proof of concept; that
is, are we being realistic in
what we’re trying to
demonstrate
UPM,
Fokus
3 Applied Machine Learning
techniques are not
adequate
WP3 Identify preliminary results
and explore related but
still appropriate
techniques
UNITN
4 Flexibility of CI to
incorporate: 1. required
architecture elements, 2.
capacity to get
performance or be
representative, 3. docking
for testing frameworks
WP6 Resulting design and
definitions has been
concluded from the
requirements of the
different demos defining a
representative middle
point. These will evolve as
new inputs are tracked
twice per month with
Vicomtech
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updates in a specific slot of
the WP6 telco
5 A Use Case has not
obtained a Proof of
Concept showing some
improvement.
WP3,
WP4
Reduce the scope of the
proof of concept
UPM
6 Sudden changes of
personnel
WP5 New personnel were
integrated. An extension
of the WP was granted.
Fokus
7 Versatile Continuous
Integration and testing
system: The current
testbed employed for
experimentation and by
the demos inside CogNet
can be too tailored to the
Rackspace APIs and the
ability to be generalized
and instantiated in other
infrastructures must be
checked
WP4,
WP6
From the deployment
code on GitHub Orange
will perform a deep
analysis of the Common
Infrastructure deploying it
on other infrastructures
from the partners. The
ability to operate with
different infrastructures
will be explored by Orange
Vic
8 Policy Engine applicability:
Universal applicability of
dynamic SUPA ECA
documents to all the
actions considered in the
demos
WP2,
WP6
The final iteration on the
design and definition of
the Policy Engine will be
concluded from the
collection of planned
actions, including a set of
actuation verbs and
required parameters,
coming from the inputs of
the different demos.
Vic
9 Testing coverage: Testing
report not only covering
scientific results but D2.1
CogNet Validation metrics
and D2.2 Technical and
WP2,
WP4,
WP5,
WP6
Generate a full picture
with the covered metrics
to span the most
representative metrics that
guarantee a complete
Vic
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CogNet Version 1.0 Page 34 of 51
non-technical
requirements
validation of the CogNet
goals.
10 Automated testing:
Framework to inject
realistic traffic to the
infrastructure and a tool
to benchmark tool
CogNet Evaluation
Metrics (KPIs)
WP6 The next step around
testing is to adopt the
Jenkins system to perform
the tests, this way the
demo could be exercised
in a programmed way.
Vic
11 Apply actuation: Uniform
integration with
endpoints for the set of
actuation verbs
WP6 Poll the intention and
track the progress on
integration with
SDN/MANO/OSS/BSS
systems
Vic
12 Easiness and suitability of
mechanism to inject
realistic traffic to the
infrastructure and a
reporting tool to compile
result for the evaluation of
CogNet Evaluation
Metrics (KPIs)
WP6 Special session is
scheduled in November
2017 in Orange Gardens
to check integration,
evaluation and
connectivity aspects of the
WP6 demos. Here the
partners will receive guide
and support for the
adoption of the Jenkins
system to perform the
tests. This way the demo
could be exercised in a
programmed way.
Vic
Table 6 Sample risks identified within the CogNet project
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4. Potential Impacts
The CogNet consortium is focused on guaranteeing a strong impact of the project achievements
in the most relevant research and industrial communities, spanning across several categories of
stakeholders in the cloud service provider, 5G and Machine learning areas by the se?? of the
following mediums:
Website and Social Media channels: aimed at promoting to a general and wide public our
research and development activity.
Communication and Dissemination: development of a converged strategy aiming at
ensuring a broad impact of our dissemination strategy through the participation to several
industrial and academic conferences and events and the publication of papers, journal
articles, white papers, brochures and posters.
Standardization: continuous monitoring and contribution to standards in the relevant
standardization bodies and working groups aims at improving the technical acceptability
of our results.
Business exploitation: as a final outcome of the research activity, the exploitation of the
Project’s results or foreground constitutes an essential first step towards the potential
commercialization of the CogNet solution.
Since the beginning of the project the dissemination and exploitation strategy has been considered
one of the key goals of CogNet. In order to establish a shared and efficient process helping to
identify, develop, review and make available content which communicates the objectives and
results of the activities in the project. The impact of the project is also crucial, exposing key
audiences, potential customers and relevant academic and research stakeholders to the CogNet
solution was essential in maximizing impact.
The dissemination plan of the CogNet partners consists of the following activities:
Published high-quality papers in major international conferences and journals in the area
of networking, security, autonomic systems, big data, data mining, data analysis and closely
related fields, to promote new ideas and concepts stemming from project activities and
outcomes.
For the academic partners, gaining significant skills in the area of big data computing, data
mining and analysis, machine learning and distributed computing. Such skills and
knowledge is currently leading to new courses to be held to undergraduate, graduate and
PhD students, summer schools, seminars and informal presentations to research partners.
For the industrial partners, the dissemination of results and prototypal implementations,
both internally and to other partner companies for industrial exploitation.
The dissemination plan also includes:
Press releases and Whitepapers; introducing project vision and key aspects of CogNet
research
Posters/brochures which was used for project dissemination by consortium partners in
networking events at conferences and within the 5GPPP coordination activities.
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4.1. Main Dissemination Activities
CogNet had chosen to set up a number of communication means, ranging from a project dedicated
web site, up to social networking channels in Twitter, LinkedIn, Facebook and Youtube. All those
means are intended to report on CogNet progresses and convey our research findings to the
public.
The CogNet website has been designed and implemented in order to provide brief yet
comprehensive information on project goals and scope, publications, results and other similar
relevant material. The design of the website is compliant with standard practices for improving
usability for the navigation and clarity over different type of fixed and mobile devices.
Key goal of the communication and dissemination activities in CogNet was to establish an efficient
and consortium-wide process to publish and get validation from the wide research community on
the major findings and results of our technical activities.
CogNet dissemination consisted of activities for project promotion as a whole, and dissemination
of specific and innovative results (e.g., scientific papers). These include scientific papers, journals
and conferences of interest, press releases, and a list of relevant industrial associations that are
interested in the project activities and outcomes. Both future and current activities are presented,
targeting different academic and industrial communities, students, stakeholders and decision
makers. Below are listed the CogNet communication channels and their respective URLs.
Communication channel URL
CogNet website http://www.cognet.5g-ppp.eu
CogNet Twitter account https://twitter.com/5GPPPCogNet
CogNet LinkedIn group https://www.linkedin.com/groups/8353951
CogNet Facebook
account
https://www.facebook.com/5GPPPCogNet/
CogNet YouTube
channel
https://www.youtube.com/channel/UCv3BjdE2XedmnSOOYLq6E_w
4.2. Publications and events participation
This section lists all the dissemination items occurred during the whole Project duration.
Publication Title Authors Event
“The Application of Machine
Learning and Data Analytics
to Network Management for
Large Scale Networks”
(invited talk)
Robert Mullins Fokus Fuseco Forum Nov, 2015
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“Deep Natural Language
Processing for Cognitive
Dialog Systems” (invited talk)
Alessandro Moschitti Deep Natural Language Processing
for Cognitive Dialog Systems Nov,
2015
“CogNet: An NFV/SDN based
architecture for Autonomic
5G Network Management
using Machine Learning”
(poster)
D. Gallico, M. Biancani,
H. Assem, D. Lopez
5G: From Myth to Reality (ETSI) Apr,
2016
“NFV Service Orchestration
and Lifecycle Management
based on Open Source
MANO” (invited talk)
Diego Lopez TMForumLive! May, 2016
"Deep Natural Language
Processing for Fact
Verification and User
Interaction” (presentation)
Alessandro Moschitti Google NLP Workshop 2016 May,
2016
“The future of 5G with
Cognitive Computing”
Haytham Assem IBM Technical Leadership Exchange
(TLE) May, 2016
“CogNet: A new architecture
featuring cognitive features”
Teodora Sandra Buda IBM Technical Leadership Exchange
(TLE) May, 2016
"Applying Machine Learning
to Intent-Based Networking "
(presentation)
Diego R. Lopez Open Platform for NFV (OPNFV)
Summit Jun, 2016
1st International Workshop
on Network Management,
Quality of Service and
Security for 5G Networks”
(organization of industrial
events)
Robert Mullins Conference Workshop hosted at
25th EuCNC 2016 Jun, 2016
“Distributional Neural
Networks for Automatic
Crossword Puzzles”
Severyn, M. Nicosia,
G.Barlacchi, A. Moschitti
53rd annual meeting of the
Association for Computational
Linguistics (ACL) Jul, 2015
“SACRY: Syntax-based
automatic crossword puzzle
resolution system”
G. Barlacchi, M.Nicosia,
A.Moschitti
53rd annual meeting of the
Association for Computational
Linguistics (ACL) Jul, 2015
“Learning to Rank Short Text
Pairs with Convolutional
Deep Neural Networks”
Severyn, A. Moschitti 38th International ACM SIGIR
Conference on Research and
Development in Information
Retrieval Aug, 2015
“Assessing the Impact of
Syntactic and Semantic
Structures for Answer
Passages Reranking” (paper)
K. Tymoshenko,
A.Moschitti
24th CIKM Oct, 2015
“Deep Neural Networks for
Named Entity Recognition in
Italian”
D. Bonadiman, A.
Severyn, A. Moschitti
2nd Italian Conference on
Computational Linguistics Dec,
2015
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"Intent-Based Networking -
And How Machine Learning
Can Bring Convergence”
P. A. Aranda Networld 2020 Strategic Research
Agenda Mar, 2016
"Parallelized unsupervised
feature selection for large-
scale network traffic analysis"
Ordozgoiti, S.Gómez
Canaval A.Mozo
24th ESANN Apr, 2016
"PSCEG: An unbiased Parallel
Subspace Clustering
algorithm using Exact Grids"
Zhu, B.Ordozgoiti,
A.Mozo
24th ESANN Apr, 2016
“Can Machine Learning aid in
delivering new Use cases and
Scenarios in 5G?”
T.S.Buda, H.Assem,
D.Lopez, M. I. Corici,
D.Raz, O.Uryupina,
R.Mullins, I.G. Ben Yahia
IEEE 5GMan May, 2016
“Agile Service Manager for
5G”
M. Mechtri, I. G. Ben
Yahia, D. Zeghlache
IEEE 5GMan May, 2016
“Emerging Management
Challenges for the 5G era:
Multi-Service Provision
through Optimal End-to-End
Resource Slicing in Virtualized
Infrastructures”
K. Tsagkaris, I. G. Ben
Yahia, A.
Georgakopoulos, P.
Demestichas
IEEE 5GMan May, 2016
“Crossword Puzzle Resolution
in Italian using Distributional
Models for Clue Similarity”
M. Nicosia, A. Moschitti 7th IIR May, 2016
“ARRAU: Linguistically-
Motivated Annotation of
Anaphoric Description”
O. Uryupina, R. Artstein,
A. Bristot, F. Cavicchio,
K.J. Rodriguez, M. Poesio
10th LREC May, 2016
“Machine Learning for
Autonomic Network
Management in a Connected
Cars Scenario”
G. Velez, M. Quartulli, A.
Martin, O. Otaegui, H.
Assem
NETS4CARS Jun, 2016
“Convolutional Neural
Networks vs. Convolution
Kernels: Feature Engineering
for Question Answering”
K. Tymoshenko, D.
Bonadiman, A. Moschitti
NAACL Jun, 2016
“KeLP at SemEval-2016 Task
3: Learning Semantic
Relations between Questions
and Answers”
S. Filice, D. Croce, A.
Moschitti, R. Basili
SemEval Jun, 2016
ConvKN at SemEval-2016
Task 3: Answer and Question
Selection for Question
Answering on Arabic and
English Fora"
A. Barron-Cedeno, D.
Bonadiman, G. Da San
Martino, S. Joty, A.
Moschitti, F. A. Al
Obaidli, S. Romeo, K.
Tymoshenko, A. Uva
SemEval Jun, 2016
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“An Energy Efficient
Architecture for 5G Network
Management”
K. Sullivan, M. Barros, A.
Martin
EuCNC 2016 Jun, 2016
“CogNet: A Network
Management Architecture
Featuring Cognitive
Capabilities”
L. Xu, H. Assem, T. S.
Buda, D. R. López, I. G.
Ben Yahia, M. Smirnov,
D. Raz, O. Uryupina, A.
Martin, A. Mozo, D.
Gallico, R. Mullins
EuCNC 2016 Jun, 2016
“Cooperative Caching in C-
RAN using Bayesian
Classification and Greedy
Placement”
B. Azpiazu, M. Quartulli,
A. Martín, I. Golaizola, B.
Sierra
EuCNC 2016 Jun, 2016
"Using Machine Learning to
Detect Noisy Neighbors in 5G
Networks"
U. Margolin, A. Mozo, B.
Ordozgoiti, D. Raz, E.
Rosensweig, I. Segall
EuCNC 2016 Jun, 2016
“LiMoSINe pipeline:
Multilingual UIMA-based NLP
platform”
O. Uryupina, B. Plank, G.
Barlacchi, F. Valverde
Albacete, M. Tsagkias,
A.Uva, A. Moschitti
54th ACL Aug, 2016
“The Rhythms of Italian Cities:
Estimating Presence Patterns
from Mobile Phone Data”
G. Barlacchi, P. Bosetti, Q.
Zhang, M. Chinazzi, S,
Bernaola, A. Vespignani,
B. Lepri
IC2S2 Jun, 2016
Machine Learning as a
Service for enabling Internet
of Things and People
Haytham Assem, Lei Xu,
Teodora Sandra Buda,
Declan O’Sullivan
Personal and Ubiquitous
Computing (PUC) Journal
Spatio-Temporal Clustering
Approach for Detecting
Functional Regions in Cities
. Haytham Assem, Lei Xu,
Teodora Sandra Buda,
Declan O’Sullivan
28th IEEE International Conference
on Tools with Artificial Intelligence
(ICTAI) 2016 Nov, 2016
ADE: An ensemble approach
for early anomaly detection
Teodora Sandra Buda,
Haytham Assem, Lei Xu
IFIP/IEEE International Symposium
on Integrated Network
Management (IM) 2017 May, 2017
5G Architecture White Paper 5G Architecture Working
group
Jul, 2016
A Fast Iterative Algorithm for
Improved Unsupervised
Feature Selection
Ordozgoiti, Bruno,
Sandra Gómez Canaval,
and Alberto Mozo
Data Mining (ICDM), 2016 IEEE 16th
International Conference on. IEEE
Dec, 2016
Deep convolutional neural
networks for detecting noisy
neighbours in cloud
infrastructure
Ordozgoiti, Bruno,
Sandra Gómez Canaval,
Alberto Mozo, Udi
Margolin, Elisha
Rosensweig, Itai Segall
Proc. ESANN. Vol. 2017 Apr, 2017
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Feature Ranking and
Selection for Big Data Sets
Ordozgoiti, Bruno,
Sandra Gómez Canaval,
and Alberto Mozo
East European Conference on
Advances in Databases and
Information Systems Aug, 2016
Using Machine Learning to
Detect Noisy Neighbors in 5G
Networks
Margolin, Udi, Alberto
Mozo, Bruno Ordozgoiti,
Danny Raz, Elisha
Rosensweig, and Itai
Segall
Networking and Internet
Architecture Oct, 2016
Spark2Fires: A New Parallel
Approximate Subspace
Clustering Algorithm
Zhu, Bo, and Alberto
Mozo
East European Conference on
Advances in Databases and
Information Systems Aug, 2016
Learning to Rank Non-
Factoid Answers: Comment
Selection in Web Forums
Kateryna Tymoshenko,
Daniele Bonadiman,
Alessandro Moschitti
CIKM 2016 (25th ACM International
on Conference on Information and
Knowledge Management) Oct,
2016
A Practical Perspective on
Latent Structured Prediction
for Coreference Resolution
Iryna Haponchyk,
Alessandro Moschitti
EACL 2017 (European Chapter of
the Association for Computational
Linguistics ) Apr, 2017
Effective Shared
Representations with
Multitask Learning for
Community Question
Answering
Daniele Bonadiman,
Antonio Uva, Alessandro
Moschitti
EACL 2017 (European Chapter of
the Association for Computational
Linguistics ) Apr, 2017
AI for SLA Management in
Programmable Networks
Imen Grida Ben Yahia,
Jaafar Bendriss, Prosper
Chemouil, Djamal
Zeghlache
International Conference on Design
of Reliable Communication
Networks 2017 Mar, 2017
Forecasting and Anticipating
SLO Breaches in
Programmable Networks
Imen Grida Ben Yahia,
Jaafar Bendriss, Djamal
Zeghlache
2017 20th Conference on
Innovations in Clouds, Internet and
Networks (ICIN) Mar, 2017
CogNitive 5G Networks:
Comprehensive Operator Use
Cases with Machine Learning
for Management Operations
Imen Grida Ben Yahia,
Jaafar Bendriss, Alassane
Samba, Philippe Dooze
2017 20th Conference on
Innovations in Clouds, Internet and
Networks (ICIN) Mar, 2017
Log-based behavioral
differencing
Maayan Goldstein,
Danny Raz, Itai Segall
ISSTA 2017 Jul, 2017
Integrated Terahertz
Communication with
Reflectors for 5G Small Cell
Networks
Michael T. Barros,
Sasitharanand
Balasubramaniam, and R.
Mullins.
IEEE Transactions on Vehicular
Technology , 2017 Dec, 2016
CogNet: An NFV/SDN based
architecture for Autonomic
5G Network Management
using Machine Learning
(poster)
Domenico Gallico (IRT),
Matteo Biancani (IRT),
Haytham Assem (IBM),
Diego Lopez (TID)
2nd Global 5G Event – “Enabling
the 5G EcoSphere” Nov, 2016
SLA enforcement Imen Grida Ben Yahia
and Jaafar Bendriss
(Orange)
Orange Exhibition days Dec, 2016
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Cognitive Services Portfolio
for 5G Network Management
Bora Caglayan, Teodora
Sandra Buda, Haytham
Assem, Imen Grida Ben
Yahia, Jaafar Bendriss,
Angel Martin, Gorka
Velez, Udi Margolin, Itai
Segall, Antonio Pastor,
Diego Lopez, Alberto
Mozo, Bruno Ordozgoiti,
Marius-Iulian Corici,
Mikhail Smirnov,
Kateryna Timoshenko,
Olga Uryupina, Joe
Tynan, Martin Tolan
2nd Workshop on Network
Management, Quality of Service
and Security for 5G Networks
colocated with EUCNC 2017 Jul,
2016
5G PPP – 5G Architecture
White Paper
5G PPP Architecture
Working Group
Jul, 2016
Dynamic Policy Based
Actuation for Autonomic
Management of Telecoms
Networks
Martin Tolan, Joe Tynan,
Angel Martin, Felipe
Mogollon
2nd Workshop on Network
Management, Quality of Service
and Security for 5G Networks
colocated with EUCNC 2017 Jul,
2016
Book chapter for 5G
European Vision
Haytham Assem, Jaafar
Bendriss, Teodora
Sandra Buda, Imen Grida
Ben Yahia, Diego Lopez,
Udi Margolin, Angel
Martin, Alberto Mozo,
Marouane Mechteri,
Kieran Sullivan, Martin
Tolan
RCMC: Recognizing Crowd
Mobility Patterns in Cities
based on Location Based
Social Networks Data
Haytham Assem,
Teodora Sandra Buda,
Declan O’Sullivan
Journal: ACM Transactions on
Intelligent Systems and Technology
(TIST)
Discovering New Socio-
demographic Regional
Patterns in Cities
Haytham Assem, Lei Xu,
Teodora Sandra Buda,
Declan O’Sullivan
LBSN Workshop, SIGSPATIAL 2016,
ACM 9thInternational Conference
Machine Learning as a
Service for enabling Internet
of Things and People
Haytham Assem,
Teodora Sandra Buda,
Declan O’Sullivan
Journal: Personal and Ubiquitous
Computing (PUC) Journal
RCMC: Recognizing Crowd
Mobility Patterns in Cities
based on Location Based
Social Networks Data
Haytham Assem,
Teodora Sandra Buda,
Declan O’Sullivan
Journal: ACM Transactions on
Intelligent Systems and Technology
(TIST)
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Spatio-Temporal Clustering
Approach for Detecting
Functional Regions in Cities
Haytham Assem, Lei Xu,
Teodora Sandra Buda,
Declan O’Sullivan
IEEE ICTAI (International
Conference on Tools for Artificial
Intelligence) 2016 Nov, 2016
Cognitive Applications and
Their Supporting Architecture
for Smart Cities
Haytham Assem, Lei Xu,
Teodora Sandra Buda,
Declan O’Sullivan
Journal: Big Data Analytics for
Sensor-Network Collected
Intelligence
ADE: An ensemble approach
for early anomaly detection
Teodora Sandra Buda,
Haytham Assem, Lei Xu
IFIP/IEEE International Symposium
on Integrated Network
Management (IM) 2017 May, 2017
Instantaneous Throughput
Prediction in Cellular
Networks: Which Information
Is Needed?
Alassane Samba (Orange
Labs, France), Gwendal
Simon (Telecom
Bretagne, France),
Philippe Dooze (Orange
Labs, France), Yann
Busnel (Crest (Ensai) /
Inria Rennes, France),
Alberto Blanc (Telecom
Bretagne, France)
IFIP/IEEE International Symposium
on Integrated Network
Management May, 2017
Don't you understand a
measure? Learning it:
structured prediction for
Coreference Resolution using
its evaluation measure as a
loss function
Iryna Haponchyk
(UNITN), Alessandro
Moschitti (UNITN)
ACL (Association for Computational
Linguistics) 2017 Aug, 2017
RelTextRank: An Open Source
Framework for Building
Relational Syntactic-Semantic
Text Pair Representations
Kateryna Tymoshenko
(UNITN), Alessandro
Moschitti (UNITN),
Massimo Nicosia
(UNITN) and Aliaksei
Severyn (Google)
ACL (Association for Computational
Linguistics) 2017 Aug, 2017
Self-Crowdsourcing Training
for Relation Extraction
Azad Abad(UNITN),
Moin Nabi (UNITN) and
Alessandro Moschitti
(UNITN)
ACL (Association for Computational
Linguistics) 2017 Aug, 2017
Annotating a broader range
of anaphoric phenomena, in a
variety of genres: the ARRAU
Corpus
Olga Uryupina (UNITN),
Ron Artstein, Antonella
Bristot, Federica
Cavicchio, Francesca
Delogu, Kepa J.
Rodriguez, Massimo
Poesio
Journal: Journal of Natural
Language Engineering
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A fast iterative algorithm for
improved unsupervised
feature selection
Bruno Ordozgoiti (UPM),
Sandra Gómez Canaval
(UPM), Alberto Mozo
(UPM)
2016 IEEE 16th International
Conference on Data Mining (ICDM)
Dec, 2017
Deep convolutional neural
networks for detecting noisy
neighbours in cloud
infrastructure
Bruno Ordozgoiti (UPM),
Sandra Gómez Canaval
(UPM), Alberto Mozo
(UPM), Udi Margolin
(Nokia), Elisha
Rosensweig (Nokia), Itai
Segall (Nokia)
25th European Symposium on
Artificial Neural Networks,
Computational Intelligence and
Machine Learning Apr, 2016
Probabilistic Leverage Scores
for Parallelized Unsupervised
Feature Selection
Bruno Ordozgoiti (UPM),
Sandra Gómez Canaval
(UPM), Alberto Mozo
(UPM)
14th International Work-
Conference on Artificial Neural
Networks 2017 (IWANN) Jun, 2017
CogNet: Network
Management Architecture
Featuring Cognitive
Capabilities
Lei Xu, Haytham Assem,
Imen Grida Ben Yahia,
Teodora Sandra Buda,
Angel Martin, Domenico
Gallico, Matteo Biancani,
Antonio Pastor, Pedro A.
Aranda, Mikhail Smirnov,
Danny Raz, Olga
Uryupina, Alberto Mozo,
Bruno Ordozgoiti,
Marius-Iulian Corici, Pat
O’Sullivan, Robert
Mullins.
EUCNC 2016 Jun, 2016
Virtualization and 5G
(Tutorial)
Fabrizio Granelli (UNITN) European Wireless 2017 May, 2017
Enabling 5G architectures
through Software Defined
Networking (Tutorial)
Fabrizio Granelli (UNITN) IEEE CAMAD 2017 Jun, 2017
Deep Learning and Structural
Kernels for Semantic
Inference on Web Data
(Invited talk)
Alessandro Moschitti
(UNITN)
SLTC 2016 Nov, 2016
Machine Learning for 5G
applications
Olga Uryupina (UNITN) NetCla: ECML-PKDD 2016 Network
Classification Challenge Sep, 2016
Sep, 2016
Cognitive modules
supporting Network
Management
Teodora Sandra Buda
(IBM)
SAP Industrial Innovation Event 27
July 2017
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Urban Mobility Awareness for
Network Demand Prediction
in Smart Cities
Haytham Assem (IBM) All Ireland Smart Cities Forum 12
September 2017
Urban Mobility Awareness for
Network Management
Haytham Assem (IBM) Irish Management Institute
Postgrad Event 5th October 2017
Urban Mobility Awareness
and Dense Urban Area demos
Haytham Assem,
Teodora Sandra Buda,
Bora Caglayan, Jason
Lloyd (IBM)
H2020 SELIS GA 25th October 2017
Urban Mobility Awareness
and Performance Anomaly
Detection for Network
Management
Teodora Sandra Buda
(IBM), Imen Grida Ben
Yahia (Orange)
Orange Annual Exhibition 5-7
December 2017
Cognitive Network
Management
Imen Grida Ben Yahia
(Orange)
9th FUSECO FORUM 09-10,
November 2017
Efficient Weighted Model
Integration via SMT-Based
Predicate Abstraction
Paolo Morettin (UNITN),
Andrea Passerini
(UNITN), and Roberto
Sebastiani (UNITN)
IJCAI 2017 August 19-25, 2017
Learning Contextual
Embeddings for Structural
Semantic Similarity using
Categorical Information
Massimo Nicosia
(UNITN) and Alessandro
Moschitti (UNITN)
CoNLL August 3-4, 2017
Accurate Sentence Matching
with Hybrid Siamese
Networks
Massimo Nicosia
(UNITN) and Alessandro
Moschitti (UNITN)
CIKM November 6-10, 2017
Neural Sentiment Analysis for
a Real-World Application
Daniele Bonadiman
(UNITN), Giuseppe
Castellucci (Almawave),
Andrea Favalli
(Almawave), Raniero
Romagnoli (Almawave)
and Alessandro
Moschitti (UNITN)
Clic-it December 11-13, 2017
Predicting Land Use of Italian
Cities using Structural
Semantic Models
Gianni Barlacchi (UNITN),
Bruno Lepri and
Alessandro Moschitti
(UNITN)
Clic-it December 11-13, 2017
Structural Semantic Features
for Land Use Classification
(Submitted)
Gianni Barlacchi (UNITN),
Bruno Lepri(FBK) and
Alessandro Moschitti
(UNTIN)
WWW2018 April 23-27, 2018
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CogNet Version 1.0 Page 45 of 51
Syntactic and Semantic
Structures for Answer
Passages Reranking (UNDER
REVIEW)
Kateryna Tymoshenko
(UNITN), Alessandro
Moschitti (UNITN)
Journal: TOIS (ACM Transactions on
Information Systems)
Autonomous Crowdsourcing
through Human-Machine
Collaborative Learning
Azad Abad (UNITN),
Moin Nabi (UNITN) and
Alessandro Moschitti
(UNITN)
SIGIR 2017 August 7-11, 2017
Collaborative Partitioning for
Coreference Resolution
Olga Uryupina (UNITN)
and Alessandro
Moschitti (UNITN)
CoNLL August 3-4, 2017
ST-DenNesFus: Deep Spatio-
Temporal Dense Networks for
Network Demand Prediction
(Under Review)
Haytham Assem, Bora
Caglayan, Teodora
Sandra Buda, Declan
O'Sullivan (IBM)
Journal: IEEE Transactions on
Knowledge and Data Engineering
(TKDE) 2017
DeepAD: A Generic
Framework based on Deep
Learning for Time Series
Anomaly Detection (Under
Review)
Teodora Sandra Buda,
Bora Caglayan, Haytham
Assem (IBM)
PAKDD 2018 June 3-6, 2018
5G PPP – 5G Architecture
White Paper version 2
Teodora Sandra Buda,
Bora Caglayan, Haytham
Assem (IBM)
LAMB-DASH: A DASH-HEVC
adaptive streaming algorithm
in a sharing bandwidth
environment for
heterogeneous contents and
dynamic connections in
practice
Angel Martin, Roberto
Viola, Josu Gorostegui,
Mikel Zorrilla, Julian
Florez and Jon
Montalban
(VICOMTECH)
Journal: Springer Journal of Real-
Time Image Processing 23 October
2017
SaW: Video Analysis in Social
Media with Web-based
Mobile Grid Computing
Mikel Zorrilla, Julián
Flórez, Alberto Lafuente,
Angel Martin, Jon
Montalbán, Igor G.
Olaizola, Iñigo Tamayo
(VICOMTECH)
IEEE Transactions on Mobile
Computing 26 October 2017
Hybrid MEC and Client
Adaptation for Fair and
Efficient Media Streaming in
SDR Mobile Networks
(Submitted)
Angel Martin, Roberto
Viola, Josu Gorostegui,
Mikel Zorrilla, Julian
Florez and Jon
Montalban
(VICOMTECH)
IEEE Transactions on Circuits and
Systems for Video Technology 20
November 2017
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Cognitive Management
Architecture for QoE-aware
delivery of media services in
5G Networks (Submitted)
Angel Martin, Jon Egaña,
Julian Florez, Jon
Montalban, Marco
Quartulli, Roberto Viola
and Mikel Zorrilla.
(VICOMTECH)
IEEE Transactions on Broadcasting
22 December 2017
Iterative column subset
selection
Bruno Ordozgoiti (UPM),
Sandra Gómez Canaval
(UPM), Alberto Mozo
(UPM)
Journal: Knowledge and
Information Systems (KAIS)
A distributed and quiescent
max-min fair algorithm for
network congestion control
(PUBLISHED).
Alberto Mozo (UPM),
José Luis López-Presa
(UPM), Antonio
Fernández Anta (IMDEA
Networks)
Journal: Expert Systems with
Applications
Forecasting short-term data
center network traffic
dynamics with convolutional
neural networks (to be
published in 2018)
Alberto Mozo (UPM),
Bruno Ordozgoiti (UPM),
Sandra Gómez Canaval
(UPM)
Journal: PLOS ONE
Mining logical theories in
feature space
Andrea Passerini
(UNITN)
SMiLee February 3-4, 2016
Where are you going? An
overview on machine learning
models for human mobility
Gianni Barlacchi (UNITN) PyData Italy 2016 April 19, 2016
CIKM Data Mobility
Challenge
Gianni Barlacchi (UNITN) CIKM 2017 November 6, 2017
Master course: Massively
Parallel Machine Learning
Alberto Mozo (UPM) September 2017 – January 2018
Introduction to deep learning Bruno Ordozgoiti,
Alberto Mozo (UPM)
Workshop at Universidad
Politécnica de Madrid 4 - 11
October 2017
Applying Cognitive
Techniques to Enable Intent-
Based Networking
Diego R. Lopez IEEE 5G Summit at UNET 2017 11
May 2017
On the Dialectics of Intent,
and how it applies to next-
generation network
management
Diego R. Lopez EUCNC 2017 12-15 June 2017
Network Service
Management in the Days of
the Software Network
Diego R. Lopez SDN World Congress 9-13 October
2017
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4.3. Collaboration with other EU Groups and Projects
This section reports all of the collaborations with other 5G-PPP Projects. Throughout the life of
CogNet one of the main focuses was the dissemination to a wider audience. A particular emphasis
was on interactions with other 5G-PPP projects which included presentations, networking events,
published papers and workshops. Below is a list of the main activities carried out during the whole
Project duration.
Event Activity
Steering Board meeting, September
2016, Brussels
Presentation about the progress of CogNet and the
Network Management & QoS Working Group.
Steering Board meeting, December
2016, London
Presentation on project updates and the project’s
position was conveyed on various issues related to
5G-PPP.
7th FUSECO Forum, November 2016,
Berlin
Panel on open source approaches to NFV
orchestration, where CogNet results were
mentioned.
5GMan 2017, May, 2017 Workshop organized with createNet which are part
of Coherent and Sesame project.
Steering Board and Technological Board
meetings
Contributions to Phase-2 cartography and related
schematics
Chair of Working Group on 'Network
Management & QoS’
Updated Terms of Reference, identified Targeted
Outputs, regular updates to WG members (Phase-
1 and Phase-2 projects)
Discussions with 5G-Media project
(Phase-2) on using CogNet’s smart
engine
Email exchanges and telcos.
ICT Proposers Day in Budapest Informal updates/discussions with various 5G-PPP
projects during ICT Proposers Day in Budapest.
Table 4-1 Collaboration and liaisons with other 5Gppp projects
CogNet is also chairing the Network Management Working Group and hosted a Network
Management Workshop at EuCNC 2017 in Oulu, Finland. This was held in conjunction with the
SelfNet 5G-PPP project (https://selfnet-5g.eu/tag/5g-ppp/) and the title of the workshop was “2nd
Network Management, Quality of Service and Security for 5G Networks”. The reason for the
workshop was to show case the work of the Network Management, Quality of Service and Security
Working Group of the EU 5G-PPP and to present the newly developed whitepaper on these same
topics as developed by the projects involved in the Working Group.
The workshop brought together the various contributing projects within the 5G-PPP that are
involved in this working group and other interested parties (projects and/or organisations) which
have a common interest in the development and progression of the following:
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• Network Management
• Security
• Quality of Service
As a result of this workshop CogNet and Selfnet created a joint interest on these topics, and have
also established a first contact with the Charisma project (http://www.charisma5g.eu/) that has
been further explored afterwards.
In the summer of 2017 CogNet had an article published in the EURESCOM message ‘Smarter
networks through machine learning’ this article disseminated the results of the project to date. Also
in the publication was an interview with Dr. Michael Barros on Network management in 5G. Within
the later end of the project, CogNet was given the opportunity to publish a chapter in the book
title a 5G European Vision. Most of the consortium contributed to the chapter showcasing some of
the work carried out by the CogNet project.
CogNet is currently collaborating with the 5G-PPP Phase II project “5GMEDIA”
(http://www.5gmedia.eu/). Through the work carried out by CogNet and shared through the
various working groups (Steering Board, Technical Board and the Network Management and QoS
WG) a relationship has sprung up where the outputs of CogNet may be able to provide a spring
board for the 5GMedia project. This collaboration is currently ongoing, and it is envisioned that it
will continue past the finish date of CogNet so that we can provide as much assistance as possible.
CogNet is part of the Architecture Working Group and as such has contributed to the 1st and 2nd
release of the 5G Architecture White Paper. The first release has been published on July 2016, while
the second release will be published in Q4 2017 (https://5g-ppp.eu/white-papers/) and to the
“European 5G Annual Journal 2017” that presents the achievements of 5G PPP phase 1 projects
after two years after their launch and has been released on September 2017 (https://5g-
ppp.eu/annual-journal/).
4.4. Exploitation of Project’s foreground
The consortium final exploitation strategy has been periodically captured throughout the life time
of the project and has the final strategy documented in the deliverable “D7.9 - Final Business
Exploitation Plan” where each partner has provided a plan to exploit the achievements of the
CogNet project taking into account the latest development brought in the different use cases and
scenarios. This deliverable not only covers the intensions of the industrial partners where they are
planning on introducing components of CogNet directly into their portfolios but also the academic
partners who are planning on updated/creating new modules at both under and post graduate
level based on the outcomes of CogNet.
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5. Project Details
Project Website http://www.cognet.5g-ppp.eu/
Open source Repository https://github.com/CogNet-5GPPP
GitHub Repository https://github.com/CogNet-5GPPP
Twitter https://twitter.com/5GPPPCogNet
Facebook https://www.facebook.com/5GPPPCogNet/
YouTube https://www.youtube.com/channel/UCv3BjdE2XedmnSOOYLq6E_w
Coordinating Partner Telecommunications Software & Systems Group (TSSG),
Waterford Institute of Technology West Campus,
Carriganore,
Co. Waterford,
X91 P20H,
Ireland.
Email lists [email protected]
Conferences bridges GoTo Meeting
Cisco WebEx Meeting Center
Skype
Document Repositories
(private, requires
activation)
http://redmine.cognet.5g-ppp.eu/
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5.1. Meeting Metrics
Kick off meeting 16th July 2015
Face to face technical & plenary meetings 9 (all partners involved)
Workshops 8 (not all partners involved in all workshops)
WP1 weekly meetings 87 up to the 12/2017
WP2 meetings 44 up to the 04/2017
WP3 meetings 22 up to the 08/2017
WP4 meetings 18 up to the 08/2017
WP5 meetings 12 up to the 10/2017
WP6 meetings 52 up to the 12/2017
Final Review 14th March 2018
Meeting Minutes Repositories http://redmine.cognet.5g-ppp.eu/
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6. References
[1] T. S. Buda, H. Assem, L. Xu, D. Raz, U. Margolin, E. Rosensweig, D. R. Lopez, M.-I. Corici, M.
Smirnov, R. Mullins, O. Uryupina, A. Mozo, B. Ordozgoiti, A. Martin, A. Alloush, P. O'Sullivan and
I. G. B. Yahia, Can Machine Learning aid in delivering new Use cases and Scenarios in 5G?,
5GMAN Workshop, 2016 IEEE/IFIP Network Operations and Management Symposium (NOMS),
2016.
[2] M. Sanchez, A. Asadi, M. Draxler, R. Gupta, V. Mancuso, A. Morelli, A. De La Oliva and V.
Sciancalepore, Tackling the Increased Density of 5G Networks: The CROWD Approach, IEEE
81st Vehicular Technology Conference (VTC Spring), 2015.
[3] L. Jiang, G. Feng and S. Qin, Cooperative content distribution for 5G systems based on
distributed cloud service network, IEEE International Conference on Communication Workshop
(ICCW), 2015.
[4] S. Jeon, D. Corujo and R. L. Aguiar, Virtualised EPC for on-demand mobile traffic offloading in
5G environments, IEEE Conference on Standards for Communications and Networking (CSCN),
2015.
[5] L. Xu, H. Assem, I. G. B. Yahia, T. S. Buda, A. Martin, D. Gallico and M. B. e. al., CogNet: A network
management architecture featuring cognitive capabilities., IEEE European Conference on
Networks and Communications (EuCNC), 2016.
[6] ETSI, Network Functions Virtualisation, An Introduction, Benefits, Enablers, Challenges and Call
for Action. https://portal.etsi.org/nfv/nfv_white_paper.pdf, 2012.
[7] A. Kejariwal, Introducing practical and robust anomaly detection in a time series, Twitter
Engineering Blog. Web, vol. 15., 2015.