Workload Stability-Aware Virtual Machine Consolidation Using Adaptive Harmony Search in Cloud Datacenters

Owing to the increasing complexity of managing IT infrastructure caused by rapid technological advancements, organizations are transforming their datacenter management environments from on-premises cloud. Datacenters operating in cloud environment have large amounts infrastructure, such as servers, storage devices, and network equipment, operational on all days year, thus causing power overconsumption problems. However, efforts reduce consumption not first priority datacenters seek stable operation avoid violating service level agreements. Therefore, a research model that reduces while enabling utilizing virtual machine (VM) consolidation is proposed here. To obtain optimal solution for VM model, an adaptive harmony search methodology developed, which expends less effort set parameters compared existing methods. Comparative experiments were conducted validate accuracy performance model. As result, Original (HS) showed better than heuristic algorithm, novel self-adaptive (NS)-HS best result among Adaptive HS. In addition, when considering workload stability, it results terms (DC) stability otherwise.