Energy and Network Aware Workload Management for Geographically Distributed Data Centers

TL;DR

This paper presents a game theory-based workload management framework for geo-distributed data centers that optimizes energy costs, data transfer costs, and queueing delays simultaneously, leading to more cost-effective cloud operations.

Contribution

It introduces a comprehensive, holistic approach to workload scheduling that considers multiple cost factors and heterogeneity in data center resources, which was lacking in prior work.

Findings

The proposed framework reduces overall cloud operating costs more effectively than existing methods.

It accounts for diverse factors like energy prices, renewable energy, and network costs in workload scheduling.

Simulations demonstrate significant cost savings with the new approach.

Abstract

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.