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Automated Software Engineering

, Volume 26, Issue 1, pp 125–159 | Cite as

Cost-aware scheduling for ensuring software performance and reliability under heterogeneous workloads of hybrid cloud

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

Abstract

Cloud computing is a rapidly growing paradigm in software engineering that offers different services. The hybrid cloud is the best choice for the enterprise to benefit by taking resources on lease from the public cloud only if private cloud resources are not sufficient. However, the key is how to provide better cloud services and improve software performance in the hybrid cloud for software engineers. In this paper, the efficient job scheduling method in the private cloud is proposed by considering the heterogeneity of hybrid cloud resources to guarantee the software performance and reliability. The experimental results show that the efficient job scheduling method can effectively reduce the average job response time and improve the system throughput. Moreover, the task scheduling method based on BP neural network in the hybrid cloud is proposed by considering both the cost and deadline constraints to ensure the quality of service (QoS) for software. The experimental results show that the task scheduling method can improve the QoS, maximize the resources utilization of private cloud and minimize the cost of hybrid cloud resources.

Keywords

Hybrid cloud Heterogeneous workloads Job scheduling Software performance 

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 Fundamental Research Funds for the Central Universities (WUT No.2017-YB-029), the Opening Project of State Key Laboratory of Digital Publishing Technology, the Opening Project of State Key Laboratory of Software Development Environment, Beihang University. 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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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.State Key Laboratory of Digital Publishing TechnologyBeijingPeople’s Republic of China
  3. 3.State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijingPeople’s Republic of China

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