Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center

Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model. However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment. Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance. In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in Cloud Environment. In this paper, idea of MOPSO is extended with three distinct features such as Largest Processing Time (LPT) rule is applied to improve load balancing across the resources which leads to energy saving in Cloud Environment; Epsilon Fuzzy Dominance technique is used to select solutions near to Pareto front which improves the diversity of Pareto optimal solutions; and Discrete PSO along with Composite Mutation strategy in the proposed algorithm help to provide better convergence than existing approaches. Hence, the proposed algorithm produced better results than other existing algorithm such as GA and heuristics-based approach.

Automated summary

This paper proposes a technique using multi-objective particle swarm optimization and composite mutation to improve energy efficiency in cloud environments and minimize service level agreement violations. By using the longest processing time rule, Epsilon Fuzzy Dominance technique, and discrete particle swarm optimization, better results can be achieved compared to existing algorithms.