Energy Efficient Geographical Load Balancing via Dynamic Deferral of Workload

TL;DR

This paper proposes a novel energy-efficient geographical load balancing method that uses dynamic workload deferral and migration, leveraging SLA flexibility to reduce costs while meeting latency requirements.

Contribution

It introduces an offline formulation and online algorithms for workload assignment and migration that adapt to electricity price fluctuations, achieving significant cost savings.

Findings

20-30% cost savings demonstrated on MapReduce traces.

Dynamic deferral and migration outperform greedy approaches.

Effective differentiation of workloads based on SLA constraints.

Abstract

With the increasing popularity of Cloud computing and Mobile computing, individuals, enterprises and research centers have started outsourcing their IT and computational needs to on-demand cloud services. Recently geographical load balancing techniques have been suggested for data centers hosting cloud computation in order to reduce energy cost by exploiting the electricity price differences across regions. However, these algorithms do not draw distinction among diverse requirements for responsiveness across various workloads. In this paper, we use the flexibility from the Service Level Agreements (SLAs) to differentiate among workloads under bounded latency requirements and propose a novel approach for cost savings for geographical load balancing. We investigate how much workload to be executed in each data center and how much workload to be delayed and migrated to other data centers for energy saving while meeting deadlines. We present an offline formulation for geographical load balancing problem with dynamic deferral and give online algorithms to determine the assignment of workload to the data centers and the migration of workload between data centers in order to adapt with dynamic electricity price changes. We compare our algorithms with the greedy approach and show that significant cost savings can be achieved by migration of workload and dynamic deferral with future electricity price prediction. We validate our algorithms on MapReduce traces and show that geographic load balancing with dynamic deferral can provide 20-30% cost-savings.