An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment (original) (raw)

Abstract

The development of Internet of Things leads to an increase in edge devices, and the traditional cloud is unable to meet the demands of the low latency of numerous devices in edge area. On the hand, the media delivery requires high-quality solution to meet ever-increasing user demands. The edge cloud paradigm is put forward to address the issues, which facilitates edge devices to acquire resources dynamically and rapidly from nearby places. However, in order to complete as many tasks as possible in a limited time to meet the needs of users, and to complete the consistency maintenance in as short a time as possible, a two-level scheduling optimization scheme in an edge cloud environment is proposed. The first-level scheduling is by using our proposed artificial fish swarm-based job scheduling method, most jobs will be scheduled to edge data centers. If the edge data center does not have enough resource to complete, the job will be scheduled to centralized cloud data center. Subsequently, the job is divided into same-sized tasks. Then, the second-level scheduling, considering balance load of nodes, the edge cloud task scheduling is proposed to decrease completion time, while the centralized cloud task scheduling is presented to reduce total cost. The experimental results show that our proposed scheme performs better in terms of minimizing latency and completion time, and cutting down total cost.

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Acknowledgments

The work was supported by the National Natural Science Foundation (NSF) under grants (Nos. 61873341and 61672397), Application Foundation Frontier Project of WuHan (No. 2018010401011290), and Open Project of Anhui Province Key Laboratory of Special Heavy Load Robot (TZJQR001-2020). 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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Authors and Affiliations

  1. AnHui Province Key Laboratory of Special Heavy Load Robot, Ma Anshan, 243032, People’s Republic of China
    Chunlin Li
  2. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China
    Chunlin Li, Chengyi Wang & Youlong Luo

Authors

  1. Chunlin Li
  2. Chengyi Wang
  3. Youlong Luo

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Correspondence toYoulong Luo.

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Li, C., Wang, C. & Luo, Y. An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment.J Supercomput 76, 6941–6968 (2020). https://doi.org/10.1007/s11227-019-03133-9

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