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Wireless Personal Communications

, Volume 107, Issue 1, pp 169–192 | Cite as

Load Balance Based Job Scheduling in Geo-Distributed Clouds

  • Chunlin LiEmail author
  • Jianhang Tang
  • Youlong Luo
Article
  • 51 Downloads

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.

Keywords

Geo-distributed clouds Queuing theory Job scheduling Logistic regression 

Notes

Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under grants (No.61672397, No. 61873341), Application Foundation Frontier Project of WuHan (No. 2018010401011290), the Opening Project of State Key Laboratory of Digital Publishing Technology, Opening Project of Jiangsu Key Laboratory of Meteorological Observation and Information Processing. Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Digital Publishing TechnologyBeijingChina
  2. 2.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  3. 3.Jiangsu Key Laboratory of Meteorological Observation and Information ProcessingNanjing University of Information Science and TechnologyNanjingChina

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