A Novel Energy Efficient and SLA-Aware Approach for Cloud Resource Management

Abstract

Server virtualization is a well-known technique for virtual machine (VM) placement and consolidation and has been studied extensively by several researchers. This article presents a novel approach called aiCloud that advocates segmentation of hosts or physical machines (PMs) into four different classes that facilitates quick selection of PMs to reduce the time required to search host machines, called host search time (HST). The framework also introduces VM_Acceptance_State, a condition that avoids host overloading, which leads to significant reduction of SLA time per active host (SLATAH) that in turn reduces SLA violation (SLAV). The performance of aiCloud has been compared with other approaches using standard workload traces. Empirical evaluation presented shows that aiCloud has least HST and outperforms other approaches in terms of SLA violations and ESV (Energy and SLA Violation) and therefore may be an attractive strategy for efficient management of cloud resources.