SE-PSO: Resource Scheduling Strategy for Multimedia Cloud Platform Based on Security Enhanced Virtual Migration

Abstract

In the multimedia cloud platform, the resource scheduling performance directly affects the energy consumption, resource utilization of the active physical machine (PM) and virtual machine (VM) security. Besides, service level agreement (SLA) violation rate also fluctuates with the strategy. Many optimization methods have been launched to cope with this scheduling task, while none of them accommodate all the above aspects in a uniform manner to our best knowledge. In this paper, aiming at optimizing the four sides performance, we propose a new resource scheduling strategy called Security Enhanced Particle Swarm Optimization (SE-PSO) based on VM migration which uses Particle Swarm Optimization (PSO) as a kernel part. Firstly, the inertia factor and the learning factor are dynamically adapted to improve the search performance of SE-PSO. Then, by periodically predicting physical hotspots with the exponential smoothing model, we reduce unnecessary migrations and thus minimize the VM migration security risk. Finally, roulette wheel idea is applied to achieve long-term optimization of the platform resources. The experiments conducted in CloudSim with real-world dataset also show that SE-PSO has a good overall performance in energy consumption, resource utilization, SLA violation rate and migration security compared with the mainstream PSO algorithm.