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Cluster Computing

, Volume 21, Issue 2, pp 1331–1348 | Cite as

Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud

  • Li ChunlinEmail author
  • Tang Jianhang
  • Luo Youlong
Article

Abstract

In the paper, the distributed scheduling optimization model for resource-intensive mobile application is proposed. Lagrangian method is applied to achieve distributed scheduling optimization in hybrid cloud. By decomposing the Kuhn–Tucker conditions into different roles of mobile user, public cloud supplier and local cloud supplier, the scheduling optimization problem in hybrid cloud is converted into a distributed problem. The system design and example of distributed scheduling optimization for resource intensive mobile application is also given. The local or public cloud provider uses service-level agreement (SLA) in determining the share of resources to be allocated to the mobile user. The distributed scheduling optimization algorithm for resource intensive mobile application is proposed, which includes three parts: local cloud agent scheduling optimization, public cloud service scheduling and mobile application QoS optimization. The experiments study how data size, request arrival rate, number of mobile users and mobility have effect on the proposed algorithm and other related works.

Keywords

Scheduling optimization Resource-intensive mobile application QoS-aware Hybrid cloud 

Notes

Acknowledgements

The authors thank the editors and the anonymous reviewers for their helpful comments and suggestions. The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61672397, 61472294), the Fundamental Research Funds for the Central Universities (WUT No. 2017-YB-029), Wuhan University of Technology and Program for the High-end Talents of Hubei Province. 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 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.Hebei Engineering Technology Research Center for IOT Data acquisition & ProcessingNorth China Institute of Science and TechnologyHebeiPeople’s Republic of China
  3. 3.School of ManagementWuhan University of TechnologyWuhanPeople’s Republic of China

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