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Application of particle swarm optimization algorithm in allocating cloud resources for video on demand
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index.pdf
Date
2018
Author
Aygün, Betül
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Video streaming services whether on demand or live has become one of the most popular services used recently. However, investments made for these type of applications cause a very serious financial problem just because video type of multimedia data needs more real time storage and high data transfer than other type of multimedia data. Furthermore, for the video streaming applications, significant amount of system resource in computing is required. To tackle this problem, cloud computing emerges as a preferred technology. Cloud services organizations are becoming more and more sophisticated as they enable the organizations to offer services without investing in hardware or software. A huge number of cloud service providers offer different pricing methods for various applications in various regions. For this reason, it is of great importance that incoming service requests are assigned to appropriate cloud services with minimum cost and maximum user satisfaction (QoS). Because of issues like multiple cloud providers, different quality of service requirements, different service level agreements (SLA) and uncertainties in demand, price and availability, optimization of resource allocation has some challenges. The objective of this study is to optimize the cost and performance of video on demand services using cloud CDNs, storage and transcoders based on QoS requirements of users. In this paper, Mixed Integer Quadratic Programming (MIQP) and different variants of Particle Swarm Optimization (PSO) algorithm are used to schedule video requests to cloud resources to achieve minimum cost of cloud services and maximum of user satisfaction. Due to the nature of the problem, it is not possible to use the classic PSO, but the new algorithms which combine Binary PSO with heuristics algorithms are proposed. These algorithms are compared with LP algorithms which gives best result. The results show that proposed algorithms yield better results than the benchmarking algorithms.
Subject Keywords
Cloud computing.
,
Video-on-demand.
,
Heuristic algorithms.
,
Distributed databases.
URI
http://etd.lib.metu.edu.tr/upload/12622685/index.pdf
https://hdl.handle.net/11511/27768
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Graduate School of Informatics, Thesis
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B. Aygün, “Application of particle swarm optimization algorithm in allocating cloud resources for video on demand,” Ph.D. - Doctoral Program, Middle East Technical University, 2018.
