Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers

TLDR

Large‑scale virtualized data centers, driven by growing computational demand, consume vast energy, and dynamic VM consolidation via live migration and node sleep can reduce this consumption but must balance performance, necessitating continuous online optimization due to workload variability. The study aims to analyze the online nature of VM consolidation by proving competitive ratios for optimal deterministic algorithms and introducing adaptive heuristics based on historical VM usage. The authors develop optimal online deterministic algorithms and adaptive heuristics for VM consolidation, validated through extensive simulations on real‑world PlanetLab VM traces. The algorithms significantly reduce energy consumption while maintaining high SLA adherence. © 2011 John Wiley & Sons, Ltd.

Abstract

SUMMARY The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large‐scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy‐performance trade‐off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real‐world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.