A Combined Traffic and Workload-aware Optimized Virtual Machine Migration in Cloud Computing

Abstract

In cloud computing paradigm, Virtual Machine (VM) migration strategies play a vital role in reducing the Energy Consumption (EC) and balancing the workloads in the cloud servers. From this viewpoint, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction and Multipath Traffic Routing (OH-BAC-FUP-MTR) strategy was designed to accomplish effective load-balancing and minimize the chance of traffic congestion during VM migration in data centres. But, the tradeoffs among workload efficiency and power-gain in heterogeneous cloud servers were not analyzed. Hence in this article, a VM consolidation strategy is proposed with OH-BAC-FUP-MTR to switch the idle Physical Machines (PMs) into hibernation mode, resulting in very less EC. In this strategy, the VM migration is targeted at consolidating the VMs depending on the workload to the smaller number of PMs for reducing the power use and encouraging green computing. Initially, the PMs are split into various groups depending on their workload levels and then a new Merge-and-Split-based Coalitional Game-theoretic (MSCG) method is applied to select associates from these groups to create efficient coalitions. After that, OH-BAC-FUP-MTR is performed amongst the coalition associate for maximizing the reward of each coalition and PMs are maintained to operate in the maximum power-efficient condition. Finally, the investigational outcomes exhibit the OH-BAC-FUP-MTR-MSCG achieves a mean EC of 60.63KWh which is 28.87% less than all other classical VM migration strategies.