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Hybrid Cloud Adaptive Scheduling Strategy for Heterogeneous Workloads

Abstract

With the advent of the era of big data, many companies have taken the most important steps in the hybrid cloud to handle large amounts of data. In a hybrid cloud environment, cloud burst technology enables applications to be processed at a lower cost in a private cloud and burst into the public cloud when the resources of the private cloud are exhausted. However, there are many challenges in hybrid cloud environment, such as the heterogeneous jobs, different cloud providers and how to deploy a new application with minimum monetary cost. In this paper, the efficient job scheduling approach for heterogeneous workloads in private cloud is proposed to ensure high resource utilization. Moreover, the task scheduling method based on BP neural network in hybrid cloud is proposed to ensure that the tasks can be completed within the specified deadline of the user. The experimental results show that the efficient job scheduling approach can veffectively reduce the job response time and improve the throughput of cluster. The task scheduling method can reduce the response time of tasks, improve QoS satisfaction rate and minimize the cost of public cloud.

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Acknowledgments

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), Open Foundation of Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education (ESSCKF 2018-2), Open Research Fund of Shaanxi Key Laboratory of Network Data Analysis and Intelligent 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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Correspondence to Li Chunlin.

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Chunlin, L., Jianhang, T. & Youlong, L. Hybrid Cloud Adaptive Scheduling Strategy for Heterogeneous Workloads. J Grid Computing 17, 419–446 (2019). https://doi.org/10.1007/s10723-019-09481-3

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  • DOI: https://doi.org/10.1007/s10723-019-09481-3

Keywords

  • Hybrid cloud
  • Heterogeneous workloads
  • BP neural network
  • Job scheduling