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Resource allocation for cloud-based social TV applications using Particle Swarm Optimization

机译:使用粒子群算法为基于云的社交电视应用程序分配资源

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摘要

Social interaction of groups of users, amongst themselves and with the media content itself, is increasingly becoming popular due to the advancements in the Internet access technologies. However, multimedia resource provisioning for dispersed user groups poses a challenge and demands innovative technologies. This paper proposes a novel approach based on Particle Swarm Optimization (PSO) to optimally allocate computational and networking resources to a group of interactive users, such that the group Quality-of-Service (QoS) is maximized. We evaluate the performance of the proposed improved PSO method with respect to the state-of-the-art greedy resource allocation mechanisms and related PSO approaches. The ability to find a feasible solution (i.e., the serving probability) and the accuracy of such solutions are compared for different network topologies. The proposed method demonstrates reduced computational complexity, an up to 40% increase in the serving probability compared to the greedy methods, and up to 60 times faster convergence compared to the basic PSO approach. Overall, the comparable QoS level to the optimal solution suggests that the proposed solution efficiently allocates the resources available in the network.
机译:由于互联网访问技术的进步,用户组之间以及与媒体内容本身之间的社会交互越来越流行。但是,为分散的用户组提供多媒体资源带来了挑战,并需要创新技术。本文提出了一种基于粒子群优化(PSO)的新颖方法,可以将计算和网络资源最佳地分配给一组交互式用户,从而使该组服务质量(QoS)最大化。我们针对最新的贪婪资源分配机制和相关的PSO方法评估了提出的改进PSO方法的性能。针对不同的网络拓扑比较找到可行解决方案的能力(即服务概率)和这种解决方案的准确性。所提出的方法证明了降低的计算复杂性,与贪婪方法相比,服务概率提高了40%,并且与基本PSO方法相比,收敛速度提高了60倍。总体而言,与最佳解决方案可比的QoS级别表明,提出的解决方案有效地分配了网络中可用的资源。

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