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ANSPRO TECHNOLOGIES #7, 1st Floor, 100 ft Ring Road B.T.M 2nd Stage, Near Jayadeva Hospital, Bangaluru-7 Ph:080-64350727 Mob:8095286693 / 9886832434/ 7204005296 Email: [email protected] www.ansprotech.com IEEE 2017-18 PROJECT LIST(JAVA) Cloud Computing CODE TITLE AND ABSTRACT 18ANSP-CC-001 kBF: Towards Approximate and Bloom Filter based Key-Value Storage for Cloud Computing Systems As one of the most popular cloud services, data storage has attracted great attention in recent research efforts. Key-value (k-v) stores have emerged as a popular option for storing and querying billions of key- value pairs. So far, existing methods have been deterministic. Providing such accuracy, however, comes at the cost of memory and CPU time. In contrast, we present an approximate k-v storage for cloud-based systems that is more compact than existing methods. The tradeoff is that it may, theoretically, return errors. Its design is based on the probabilistic data structure called “bloom filter”, where we extend the classical bloom filter to support key-value operations. We call the resulting design as the kBF (key-value bloom filter). We further develop a distributed version of the kBF (d-kBF) for the unique requirements of cloud computing platforms, where multiple servers cooperate to handle a large volume of queries in a load-balancing manner. Finally, we apply the kBF to a practical problem of implementing a state machine to demonstrate how the kBF can be used as a building block for more complicated software infrastructures. 18ANSP-CC-002 A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment Developing high-quality software is essential for eBusiness organizations to cope with drastic market competition. With the development of cloud computing technologies, eBusiness systems and applications pay more attention to open endedness. In a cloud computing environment, eBusiness systems have the ability to provide information technology resources on demand. Traditional software metric methods in distributed systems and applications are technical and project driven, making the market demand and internal practical operation not perfectly balanced within a cloud-computing-based eBusiness corporation. To address this issue, this paper presents a hybrid framework based on the goal/question/metric paradigm to

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IEEE 2017-18 PROJECT LIST(JAVA)

Cloud Computing

CODE TITLE AND ABSTRACT

18ANSP-CC-001 kBF: Towards Approximate and Bloom Filter based Key-Value

Storage for Cloud Computing Systems

As one of the most popular cloud services, data storage has attracted

great attention in recent research efforts. Key-value (k-v) stores have

emerged as a popular option for storing and querying billions of key-

value pairs. So far, existing methods have been deterministic.

Providing such accuracy, however, comes at the cost of memory and

CPU time. In contrast, we present an approximate k-v storage for

cloud-based systems that is more compact than existing methods. The

tradeoff is that it may, theoretically, return errors. Its design is based

on the probabilistic data structure called “bloom filter”, where we

extend the classical bloom filter to support key-value operations. We

call the resulting design as the kBF (key-value bloom filter). We

further develop a distributed version of the kBF (d-kBF) for the unique

requirements of cloud computing platforms, where multiple servers

cooperate to handle a large volume of queries in a load-balancing

manner. Finally, we apply the kBF to a practical problem of

implementing a state machine to demonstrate how the kBF can be used

as a building block for more complicated software infrastructures. 18ANSP-CC-002 A Hybrid eBusiness Software Metrics Framework for Decision

Making in Cloud Computing Environment

Developing high-quality software is essential for eBusiness

organizations to cope with drastic market competition. With the

development of cloud computing technologies, eBusiness systems and

applications pay more attention to open endedness. In a cloud

computing environment, eBusiness systems have the ability to provide

information technology resources on demand. Traditional software

metric methods in distributed systems and applications are technical

and project driven, making the market demand and internal practical

operation not perfectly balanced within a cloud-computing-based

eBusiness corporation. To address this issue, this paper presents a

hybrid framework based on the goal/question/metric paradigm to

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evaluate the quality and efficiency of previous software products,

projects, and development organizations in a cloud computing

environment. In our approach, to support decision making at the

project and organization levels, three angular metrics are used, i.e.,

project metrics, product metrics, and organization metrics.

Furthermore, an improved radial-basis-function-based model is also

provided to manage existing projects and design new projects.

Experimental results on a well-known eBusiness organization show

that the proposed framework is effective, efficient, and operational.

Moreover, using the described decision-making algorithm, the

predicted data are very close to actual results on the software cost, the

fault rate, the development workload, etc., which are greatly helpful in

achieving high-quality software. 18ANSP-CC-003 IoT-Based Big Data Storage Systems in Cloud Computing:

Perspectives and Challenges

Internet of Things (IoT) related applications have emerged as an

important field for both engineers and researchers, reflecting the

magnitude and impact of data-related problems to be solved in

contemporary business organizations especially in cloud computing.

This paper first provides a functional framework that identifies the

acquisition, management, processing and mining areas of IoT big data,

and several associated technical modules are defined and described in

terms of their key characteristics and capabilities. Then current

research in IoT application is analyzed, moreover, the challenges and

opportunities associated with IoT big data research are identified. We

also report a study of critical IoT application publications and research

topics based on related academic and industry publications. Finally,

some open issues and some typical examples are given under the

proposed IoT-related research framework. 18ANSP-CC-004 Achieving Flexible and Self-Contained Data Protection in Cloud

Computing.

For enterprise systems running on public clouds in which the servers

are outside the control domain of the enterprise, access control that was

traditionally executed by reference monitors deployed on the system

servers can no longer be trusted. Hence, a self-contained security

scheme is regarded as an effective way for protecting outsourced data.

However, building such a scheme that can implement the access

control policy of the enterprise has become an important challenge. In

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this paper, we propose a self-contained data protection mechanism

called RBAC-CPABE by integrating role-based access control

(RBAC), which is widely employed in enterprise systems, with the

ciphertext-policy attribute-based encryption (CP-ABE). First, we

present a data-centric RBAC (DC-RBAC) model that supports the

specification of finegrained access policy for each data object to

enhance RBAC's access control capabilities. Then, we fuse DC-RBAC

and CP-ABE by expressing DC-RBAC policies with the CP-ABE

access tree and encrypt data using CP-ABE. Because CP-ABE

enforces both access control and decryption, access authorization can

be achieved by the data itself. A security analysis and experimental

results indicate that RBAC-CPABE maintains the security and

efficiency properties of the CP-ABE scheme on which it is based, but

substantially improves the access control capability. Finally, we

present an implemented framework for RBAC-CPABE to protect

privacy and enforce access control for data stored in the cloud. 18ANSP-CC-005 Context-Aware Verifiable Cloud Computing

Internet of Things (IoTs) has emerged to motivate various intelligent

applications based on the data collected by various ``things.'' Cloud

computing plays an important role for big data processing by providing

data computing and processing services. However, cloud service

providers may invade data privacy and provide inaccurate data

processing results to users, and thus cannot be fully trusted. On the

other hand, limited by computation resources and capabilities, cloud

users mostly cannot independently process big data and perform

verification on the correctness of data processing. This raises a special

challenge on cloud computing verification, especially when user data

are stored at the cloud in an encrypted form and processed for

satisfying the requests raised in different contexts. But the current

literature still lacks serious studies on this research issue. In this paper,

we propose a context-aware verifiable computing scheme based on full

homomorphic encryption by deploying an auditing protocol to verify

the correctness of the encrypted data processing result. We design four

optional auditing protocols to satisfy different security requirements.

Their performance is evaluated and compared through performance

analysis, algorithm implementation, and system simulation. The

results show the effectiveness and efficiency of our designs. The pros

and cons of all protocols are also analyzed and discussed based on

rigorous comparison.

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18ANSP-CC-006 A Unified Urban Mobile Cloud Computing Offloading Mechanism for

Smart Cities

The increasing urbanization level of the world population has driven

the development of a smart city geographic system, conceived as a

fully connected wide area characterized by the presence of a multitude

of smart devices, sensors, and processing nodes aimed at distributing

intelligence into the city. At the same time, the pervasiveness of

wireless technologies has led to the presence of heterogeneous

networks, operating simultaneously in the same city area. One of the

main challenges in this context is to provide sustainable solutions able

to jointly optimize the data transfer, exploiting heterogeneous

networks, and the data processing, exploiting heterogeneous devices,

for managing smart city applications for citizens’ communities. In this

article, the UMCC framework is developed, introducing a mobile

cloud computing model describing the flows of data and operations

taking place in the smart city. In particular, we focus on the proposal

of a unified offloading mechanism where communication and

computing resources are jointly managed, allowing load balancing

among the different entities in the environment, delegating both

communication and computation tasks in order to satisfy the smart city

application requirements. This allows us to cope with the limited

battery power and computation capacity of smart mobile devices and

plays a key role in a smart environment where wireless communication

is of utmost relevance, particularly in the mobility and traffic control

domains.

18ANSP-CC-007 The Journey of QoS-Aware Autonomic Cloud Computing

The cloud offers three main service types: infrastructure, platform, and

software. Thus, quality of service (QoS) in the cloud means efficiently

monitoring and measuring services and following service-level

agreements (SLAs) to ensure their efficient delivery. 1 However,

providing dedicated cloud services that ensure users’ dynamic QoS

requirements and avoid SLA violations is a big challenge. Currently,

cloud services are provisioned and scheduled according to resource

availability but fail to ensure expected performance. To address these

issues, cloud providers should evolve their ecosystems to fulfill the

QoS-aware requirements of each cloud component. To this end, two

important aspects must be considered that reflect the complexity cloud

management introduces: QoS-awareness and self-management. The

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QoS-aware aspect involves a service’s capacity to be aware of its

behavior, thus ensuring its elasticity, high availability, and reliability,

along with important QoS parameters (cost, time, energy, and so on).2

Self-management implies that the service can self-manage as its

environment requires. 18ANSP-CC-008 RAAC: Robust and Auditable Access Control With Multiple Attribute

Authorities for Public Cloud Storage

Data access control is a challenging issue in public cloud storage

systems. Ciphertext-policy attribute-based encryption (CP-ABE) has

been adopted as a promising technique to provide flexible, fine-

grained, and secure data access control for cloud storage with honest-

but-curious cloud servers. However, in the existing CP-ABE schemes,

the single attribute authority must execute the time-consuming user

legitimacy verification and secret key distribution, and hence, it results

in a single-point performance bottleneck when a CP-ABE scheme is

adopted in a large-scale cloud storage system. Users may be stuck in

the waiting queue for a long period to obtain their secret keys, thereby

resulting in low efficiency of the system. Although multiauthority

access control schemes have been proposed, these schemes still cannot

overcome the drawbacks of single-point bottleneck and low efficiency,

due to the fact that each of the authorities still independently manages

a disjoint attribute set. In this paper, we propose a novel heterogeneous

framework to remove the problem of single-point performance

bottleneck and provide a more efficient access control scheme with an

auditing mechanism. Our framework employs multiple attribute

authorities to share the load of user legitimacy verification.

Meanwhile, in our scheme, a central authority is introduced to generate

secret keys for legitimacy verified users. Unlike other multi-authority

access control schemes, each of the authorities in our scheme manages

the whole attribute set individually. To enhance security, we also

propose an auditing mechanism to detect which attribute authority has

incorrectly or maliciously performed the legitimacy verification

procedure. Analysis shows that our system not only guarantees the

security requirements but also makes great performance improvement

on key generation. 18ANSP-CC-009 A Lightweight Secure Data Sharing Scheme for Mobile Cloud

Computing

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With the popularity of cloud computing, mobile devices can

store/retrieve personal data from anywhere at any time. Consequently,

the data security problem in mobile cloud becomes more and more

severe and prevents further development of mobile cloud. There are

substantial studies that have been conducted to improve the cloud

security. However, most of them are not applicable for mobile cloud

since mobile devices only have limited computing resources and

power. Solutions with low computational overhead are in great need

for mobile cloud applications. In this paper, we propose a lightweight

data sharing scheme (LDSS) for mobile cloud computing. It adopts

CP-ABE, an access control technology used in normal cloud

environment, but changes the structure of access control tree to make

it suitable for mobile cloud environments. LDSS moves a large portion

of the computational intensive access control tree transformation in

CP-ABE from mobile devices to external proxy servers. Furthermore,

to reduce the user revocation cost, it introduces attribute description

fields to implement lazy-revocation, which is a thorny issue in program

based CP-ABE systems. The experimental results show that LDSS can

effectively reduce the overhead on the mobile device side when users

are sharing data in mobile cloud environments. 18ANSP-CC-010 SUPERMAN: Security Using Pre-Existing Routing for Mobile Ad hoc

Networks

The flexibility and mobility of Mobile Ad hoc Networks (MANETs)

have made them increasingly popular in a wide range of use cases. To

protect these networks, security protocols have been developed to

protect routing and application data. However, these protocols only

protect routes or communication, not both. Both secure routing and

communication security protocols must be implemented to provide full

protection. The use of communication security protocols originally

developed for wireline and WiFi networks can also place a heavy

burden on the limited network resources of a MANET. To address

these issues, a novel secure framework (SUPERMAN) is proposed.

The framework is designed to allow existing network and routing

protocols to perform their functions, whilst providing node

authentication, access control, and communication security

mechanisms. This paper presents a novel security framework for

MANETs, SUPERMAN. Simulation results comparing SUPERMAN

with IPsec, SAODV, and SOLSR are provided to demonstrate the

proposed frameworks suitability for wireless communication security.

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18ANSP-CC-011 Fast Phrase Search for Encrypted Cloud Storage

Cloud computing has generated much interest in the research

community in recent years for its many advantages, but has also raise

security and privacy concerns. The storage and access of confidential

documents have been identified as one of the central problems in the

area. In particular, many researchers investigated solutions to search

over encrypted documents stored on remote cloud servers. While many

schemes have been proposed to perform conjunctive keyword search,

less attention has been noted on more specialized searching

techniques. In this paper, we present a phrase search technique based

on Bloom filters that is significantly faster than existing solutions, with

similar or better storage and communication cost. Our technique uses

a series of n-gram filters to support the functionality. The scheme

exhibits a trade-off between storage and false positive rate, and is

adaptable to defend against inclusion-relation attacks. A design

approach based on an application’s target false positive rate is also

described. 18ANSP-CC-012 A Cross Tenant Access Control (CTAC) Model for Cloud Computing:

Formal Specification and Verification

Sharing of resources on the cloud can be achieved on a large scale,

since it is cost effective and location independent. Despite the hype

surrounding cloud computing, organizations are still reluctant to

deploy their businesses in the cloud computing environment due to

concerns in secure resource sharing. In this paper, we propose a cloud

resource mediation service offered by cloud service providers, which

plays the role of trusted third party among its different tenants. This

paper formally specifies the resource sharing mechanism between two

different tenants in the presence of our proposed cloud resource

mediation service. The correctness of permission activation and

delegation mechanism among different tenants using four distinct

algorithms (activation, delegation, forward revocation, and backward

revocation) is also demonstrated using formal verification. The

performance analysis suggests that the sharing of resources can be

performed securely and efficiently across different tenants of the

cloud. 18ANSP-CC-013

Improving Replication Results through Directory Server Data

Replication.

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DIT (Directory Information Tree), represented as a hierarchical data

structure consisting of directory which is a collection of knowledge

list. Replication is the method by which directory contents are

automatically copied from a Directory Server to one or more other

Directory Servers. Replication is handling by different replication

schemes. Replication between different supported platforms is quit

challenging task make possible in service oriented architecture. For

accessing Directory servers the Protocol that uses TCP/IP networking

stack is Lightweight Directory Access Protocol, the network protocol

that is employed to access directory server’s knowledge. Directory

server replication helps user to access information more flexibly and

speedily. In this paper we have proposed three schemes to replicate

directory server data. Replication of Directory server results in high

data availability and load balancing. These results reduce response

time for user and prove the propose scheme is provably highly

efficient and reliable. Due to more data availability the system handles

fault tolerance. 18ANSP-CC-014 Multiagent-Based Resource Allocation for Energy Minimization in

Cloud Computing Systems

Cloud computing has emerged as a very flexible service paradigm by

allowing users to require virtual machine (VM) resources on-demand

and allowing cloud service providers (CSPs) to provide VM resources

via a pay-as-you-go model. This paper addresses the CSP’s problem

of efficiently allocating VM resources to physical machines (PMs)

with the aim of minimizing the energy consumption. Traditional

energy-aware VM allocations either allocate VMs to PMs in a

centralized manner or implement VM migrations for energy reduction

without considering the migration cost in cloud computing systems.

We address these two issues by introducing a decentralized multi agent

(MA)- based VM allocation approach. The proposed MA works by

first dispatching a cooperative agent to each PM to assist the PM in

managing VM resources. Then, an auction-based VM allocation

mechanism is designed for these agents to decide the allocations of

VMs to PMs. Moreover, to tackle system dynamics and avoid

incurring prohibitive VM migration overhead, a local negotiation-

based VM consolidation mechanism is devised for the agents to

exchange their assigned VMs for energy cost saving. We evaluate the

efficiency of the MA approach by using both static and dynamic

simulations. The static experimental results demonstrate that the MA

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can incur acceptable computation time to reduce system energy cost

compared with traditional bin packing and genetic algorithm-based

centralized approaches. In the dynamic setting, the energy cost of the

MA is similar to that of benchmark global-based VM consolidation

approaches, but the MA largely reduces the migration cost. 18ANSP-CC-015 Identification and Multivariable Gain-Scheduling Control for Cloud

Computing Systems.

This paper presents the dynamic modeling and performance control of

a Web server hosted on a private cloud. The loud hosting Web server

is a variable capacity system with two control inputs:

1) the number of virtual machines (VMs), which is indicative of the

capacity of the cloud, and

2) the admission control used for regulating workload. As the

workload and the hosting conditions change frequently, the linear

parameter varying (LPV) framework is well suited to derive the model.

For the hosted Web server, we obtain an multiple input multiple output

(MIMO) LPV model with performance metrics such as the response

time and the throughput, which is then converted to polytopic LPV

form using tensor product transformation. Finally, we design a gain

scheduled linear quadratic regulator controller for performance

guarantees with optimal cost of VMs. The identification, validation,

and control experiments are demonstrated on the open source

Eucalyptus cloud platform. The HTTP requests are generated using

customized synthetic workload generator tool. 18ANSP-CC-016 An Efficient Cloud Market Mechanism for Computing Jobs With Soft

Deadlines.

This paper studies the cloud market for computing jobs with

completion deadlines, and designs efficient online auctions for cloud

resource provisioning. A cloud user bids for future cloud resources to

execute its job. Each bid includes: 1) a utility, reflecting the amount

that the user is willing to pay for executing its job and 2) a soft

deadline, specifying the preferred finish time of the job, as well as a

penalty function that characterizes the cost of violating the deadline.

We target cloud job auctions that executes in an online fashion, runs

in polynomial time, provides truthfulness guarantee, and achieves

optimal social welfare for the cloud ecosystem. Towards these goals,

we leverage the following classic and new auction design techniques.

First, we adapt the posted pricing auction framework for eliciting

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truthful online bids. Second, we address the challenge posed by soft

deadline constraints through a new technique of compact exponential-

size LPs coupled with dual separation oracles. Third, we develop

efficient social welfare approximation algorithms using the classic

primal-dual framework based on both LP duals and Fenchel duals.

Empirical studies driven by real-world traces verify the efficacy of our

online auction design. 18ANSP-CC-017 Predictive Control of Networked Multiagent Systems via Cloud

Computing.

This paper studies the design and analysis of networked multi agent

predictive control systems via cloud computing. A cloud predictive

control scheme for networked multivalent systems (NMASs) is

proposed to achieve consensus and stability simultaneously and to

compensate for network delays actively. The design of the cloud

predictive controller for NMASs is detailed. The analysis of the cloud

predictive control scheme gives the necessary and sufficient conditions

of stability and consensus of closed-loop networked multi agent

control systems. The proposed scheme is verified to characterize the

dynamical behavior and control performance of NMASs through

simulations. The outcome provides a foundation for the development

of cooperative and coordinative control of NMASs and its

applications. 18ANSP-CC-018 Customer-Satisfaction-Aware Optimal Multiserver Configuration for

Profit Maximization in Cloud Computing.

Along with the development of cloud computing, an increasing

number of enterprises start to adopt cloud service, which promotes the

emergence of many cloud service providers. For cloud service

providers, how to configure their cloud service platforms to obtain the

maximum profit becomes increasingly the focus that they pay attention

to. In this paper, we take customer satisfaction into consideration to

address this problem. Customer satisfaction affects the profit of cloud

service providers in two ways. On one hand, the cloud configuration

affects the quality of service which is an important factor affecting

customer satisfaction. On the other hand, the customer satisfaction

affects the request arrival rate of a cloud service provider. However,

few existing works take customer satisfaction into consideration in

solving profit maximization problem, or the existing works

considering customer satisfaction do not give a proper formalized

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definition for it. Hence, we first refer to the definition of customer

satisfaction in economics and develop a formula for measuring

customer satisfaction in cloud computing. And then, an analysis is

given in detail on how the customer satisfaction affects the profit.

Lastly, taking into consideration customer satisfaction, service-level

agreement, renting price, energy consumption, and so forth, a profit

maximization problem is formulated and solved to get the optimal

configuration such that the profit is maximized. 18ANSP-CC-019 Energy Efficient Hierarchical Resource Management for Mobile

Cloud Computing.

Mobile Cloud Computing (MCC) is a developing technology that

assists in improving the quality of the mobile services. Since the

increase in mobile resources, the researchers have taken the initiative

to take into contemplation resource sharing among heterogeneous

mobile devices. Therefore, to design a system architecture for mobility

models and resource sharing are key issues that require utmost efforts

to be solved to achieve anticipated objectives. Therefore, keeping in

view the desired goals, in this paper, we present a system architecture

based on the hierarchical resource sharing mechanism for MCC. The

proposed system architecture is divided into three domains, such as

Global Cloud Server (GCS), Local ISP Server (LIS), and Gateway

Server (GWS). Also, the novel paradigm for minimizing the delay in

the network based on deploying Foglets at each proposed algorithm of

clustering mechanism is also present. Moreover, the fuzzy rule-based

scheme is proposed to eliminate the inappropriate foglets before

deciding an optimal foglet for handover. A foglet selection scheme is

developed based on the Technique for Order of Preference by

Similarity to Ideal Solution (TOPSIS) decision mechanism. Various

parameters such as delay, jitter, Bit Error Rate (BER), packet loss,

communication cost, response time, and network load are considered

for selecting an optimal network. To check the feasibility and

performance of the proposed system architecture, the mobility scenario

is considered with a different speed of a mobile node ranging from

very high to very low. The simulation results and an analytical model

are compared with existing scheme for foglet selection by a mobile

node. From the analysis and discussion, it is shown that the proposed

system architecture helps in minimizing handover delay, packet loss,

average queuing delay, and device lifetime in a network.

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18ANSP-CC-020 Crowdsourced Multi-View Live Video Streaming using Cloud

Computing.

Advances and commoditization of media generation devices enable

capturing and sharing of any special event by multiple attendees. We

propose a novel system to collect individual video streams (views)

captured for the same event by multiple attendees, and combine them

into multi-view videos, where viewers can watch the event from

various angles, taking crowdsourced media streaming to a new

immersive level. The proposed system is called Cloud-based Multi-

View Crowdsourced Streaming (CMVCS), and it delivers multiple

views of an event to viewers at the best possible video representation

based on each viewer's available bandwidth. The CMVCS is a complex

system having many research challenges. In this paper, we focus on

resource allocation of the CMVCS system. The objective of the study

is to maximize the overall viewer satisfaction by allocating available

resources to transcode views in an optimal set of representations,

subject to computational and bandwidth constraints. We choose the

video representation set to maximize QoE using Mixed Integer

Programming. Moreover, we propose a Fairness-Based Representation

Selection (FBRS) heuristic algorithm to solve the resource allocation

problem efficiently. We compare our results with optimal and Top-N

strategies. The simulation results demonstrate that FBRS generates

near optimal results and outperforms the state-of-the-art Top-N policy,

which is used by a large-scale system (Twitch).