Energy-Aware Virtual Machine Management in Inter-datacenter Networks over Elastic Optical Infrastructure

TL;DR

This paper addresses energy-efficient virtual machine management across geographically distributed data centers by formulating an optimization problem and proposing algorithms to reduce reliance on non-renewable energy sources.

Contribution

It introduces a renewable energy-aware VM migration framework over elastic optical networks, including problem formulation and heuristic algorithms for large-scale deployment.

Findings

Up to 19.7% reduction in brown energy consumption achieved.

Formulation of VM migration as a many-manycast communication problem.

Development of heuristic algorithms for large network configurations.

Abstract

Datacenters (DCs), deployed in a large scale to support the ever increasing demand for data processing applications, consume tremendous energy. Powering DCs with renewable energy can effectively reduce the brown energy consumption. Owing to geographically distributed deployment of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. We first elicit the renewable energy-aware inter-datacenter (inter-DC) VM migration problem in an inter-DC network over the elastic optical infrastructure, present it as a many-manycast communications problem, and then formulate it as an integer linear programming problem. The objective is to minimize the total cost of the brown energy consumption of DCs in such inter-DC network via VM migration. We use CVX and Gurobi to solve this problem for small network configurations, and we propose a few heuristic algorithms that approximate the optimal solution for large network configurations. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 19.7% cost of the brown energy consumption.