Price/cooling aware and delay sensitive scheduling in geographically distributed data centers

Abstract

Servers in data centers consume large amount of energy which increase the operational cost for cloud service providers, that spend a major portion of their revenue to pay bills due to inefficient workload assignment and wastage of resources. In order to minimize the operational cost of data centers, it is essential to optimize the scheduling of the jobs. In this paper, we address the problem of inefficient cooling system, SLA violations due to network delays and processing delays in geographically distributed data centers. We propose scheduling algorithms that aim to minimize the cooling cost by exploiting the temperature variations within the data centers and electricity cost by taking advantage of time-space-varying fluctuation of electricity prices. SLA violations are aimed to be minimized by assigning jobs considering deadlines, network delays and queuing delays. Experiments conducted on CloudSim show that price/cooling aware and delay sensitive scheduling reduces the overall cost by 22% as compared to random scheduling.