Geographical Load Balancing across Green Datacenters

TL;DR

This paper proposes a load balancing strategy for micro-datacenters powered by renewable energy, using mean field techniques to model and analyze system performance and energy cost reduction.

Contribution

It introduces an asymptotic model for geographically distributed load balancing with renewable energy, providing a simple steady state solution and insights into system trade-offs.

Findings

System performance converges to the asymptotic model as datacenter number increases.

The simple model enables analysis of energy cost and load distribution trade-offs.

Mean field techniques effectively approximate complex Markov Chain models.

Abstract

"Geographic Load Balancing" is a strategy for reducing the energy cost of data centers spreading across different terrestrial locations. In this paper, we focus on load balancing among micro-datacenters powered by renewable energy sources. We model via a Markov Chain the problem of scheduling jobs by prioritizing datacenters where renewable energy is currently available. Not finding a convenient closed form solution for the resulting chain, we use mean field techniques to derive an asymptotic approximate model which instead is shown to have an extremely simple and intuitive steady state solution. After proving, using both theoretical and discrete event simulation results, that the system performance converges to the asymptotic model for an increasing number of datacenters, we exploit the simple closed form model's solution to investigate relationships and trade-offs among the various system parameters.