Self-Adaptive Consolidation of Virtual Machines For Energy-Efficiency in the Cloud

TL;DR

This paper introduces SAVE, a self-adaptive method for energy-efficient VM consolidation in cloud data centers, achieving significant energy savings through local information-based decisions.

Contribution

The paper presents a novel self-adaptive approach called SAVE for VM consolidation that reduces energy consumption using probabilistic, local information-based decisions.

Findings

SAVE reduces energy consumption by about 30% compared to VMWare DRS.

SAVE achieves 10-20% energy savings over EcoCloud.

The approach is simple to implement and effective in both simulation and real environments.

Abstract

In virtualized data centers, consolidation of Virtual Machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a Cloud Data center. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, such that the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approached called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information, therefore it is very simple to implement. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about 30% against VMWare DRS and 10-20% against EcoCloud on average.