Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Issue 1 (2nd January 2018) - Ashraf, Adnan; Porres, Ivan
Abstract: In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations. Graphical Abstract: MOACS advances the state of the art on ACO-based VM consolidation by implementing a multi-objective, multi-colony ACS algorithm. It extends our previous single-objective, single-colony ACO algorithm for VM consolidation and similar works by other researchers that implement single-objective, single-colony ACO algorithms. The proposed multi-objective, multi-colony approach eliminates the need for an aggregate objective function (AOF) and allows to combine the optimisation objectives in an appropriate manner.