Multi-objective optimisation techniques for virtual machine migration-based load balancing in cloud data centre

摘要

This paper aims to balance the load in cloud data centre (CDC) by migrating virtual machines (VM) across hosts using multi-objective optimisation techniques. The unpredictable rate of demand for the cloud services leads to load fluctuation and subsequently load imbalance in cloud data centre. Hence, to balance the load in cloud data centre, this work presents multi-objective optimisation technique-based load balancing (MOOT-LB) method. This work proposes two multi-objective optimisation techniques namely, multi-objective particle swarm optimisation (MOPSO) and multi-objective differential evolution (MODE) for load balancing the cloud data centre. These techniques identify an optimal set of hosts and set of VMs to be migrated from the source hosts and identify the target hosts for migration in an efficient way. The objectives are to minimise the frequency of VM migration and migration time. To evaluate the performance of the proposed techniques ClouSim 3.0.3 simulator is used. The performance of the proposed techniques is compared, and the results show that MOPSO-based load balancing technique achieves better performance than MODE-based load balancing technique.