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Abstract
Owing to the increasing complexity of managing IT infrastructure caused by rapid technological advancements, organizations are transforming their datacenter management environments from on-premises to the cloud. Datacenters operating in the cloud environment have large amounts of IT infrastructure, such as servers, storage devices, and network equipment, and are operational on all days of the year, thus causing power overconsumption problems. However, efforts to reduce power consumption are not the first priority as datacenters seek stable operation to avoid violating their service level agreements. Therefore, a research model that reduces power consumption of the datacenter while enabling stable operation by utilizing virtual machine (VM) consolidation is proposed here. To obtain the optimal solution for the proposed VM consolidation model, an adaptive harmony search methodology is developed, which expends less effort to set the parameters of the model compared to existing harmony search methods. Comparative experiments were conducted to validate the accuracy and performance of the proposed model. As a result, Original harmony search (HS) showed better performance than the existing heuristic algorithm, and novel self-adaptive (NS)-HS showed the best result among Adaptive HS. In addition, when considering workload stability, it showed better results in terms of datacenters (DC) stability than otherwise.
Highlights
When the stability correlation (SC) policy, which searches for a combination of virtual machine (VM) in which the workload of the physical machine machine (PM) is stably maintained, was applied, the power consumption was relatively high compared to other policies, but the effect of VM migration was relatively largely reduced
We proposed a VM consolidation model to induce stable operation of a DC by exploring VM combinations, where the sum of the workloads remain stable, to reduce the power consumption of the DC
The adaptive harmony search (HS) shows better results than the original HS, and comparative analysis with the heuristic algorithm first fit decreasing (FFD) shows that the proposed algorithm has superior performance compared to the conventional approaches
Summary
DCs of are built i the sea [5,6] It utilizes solar, wind, and renewable energy to operate DC efficiently [7]. These eco-friendly DCs show high energy efficiencies because heat management of the friendly DCs with geological advantages are utilized as backups to analyze and store da equipment requires less manual effort. As these DCs are built in remote areas, rather astomain. To improve the energy efficiencies of DCs,Many utilizing only geological advantages has limitations, andenergy efforts must be considered studies related to computing systems for efficiency have been con toducted improve the operating systems internally.
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