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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
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
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
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
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.
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
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
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
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
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
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.
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
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.
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
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
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
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
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
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
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
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.
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
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).