Load Balance Based Job Scheduling in Geo-Distributed Clouds

Abstract

In the wake of rapid development of Internet, more and more people could access it from any places in the world, which leads to the characteristic of geographical distribution of the data. Only one single cloud cannot deal with such data efficiently due to high delay and transmission cost. The geo-distributed clouds can alleviate it. However, because of the varied locations of geo-distributed clouds, how to balance the workloads of geo-distributed clouds is a crucial problem. In this paper, an efficient load balance based job scheduling in geo-distributed clouds is proposed in order to minimize the average waiting time, average response time of jobs and improve the system throughput. First, the clouds are divided into idle or busy state to get the job execution time in each cloud by Logistic regression. Then, the job scheduling problem is modeled as a $$ M/M/C $$ queue in each cloud. In addition, Lagrange Multiplier is given to derive the optimal job arrival rate of each cloud. Finally, the experimental results show that our proposed algorithm in this paper can decrease the average waiting time, average execution time and average response time of jobs, and improve system throughput.