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A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds

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Abstract

Hybrid cloud is a cost-efficient way to address the problem of insufficient resources for meeting the peak demand of its users for a service provider, which elastically scales up or down the cloud capability based on demand by combining local infrastructures and one or more public clouds. While, the combination introduces new challenges that must necessarily be addressed before adoption. To address these new challenges for improving the resource efficiency in hybrid clouds, much work tried to solve the decision problem of workload scheduling, resource provisioning, or both, where workload scheduling answers how to efficiently map workloads to available resources, and resource provisioning addresses how to optimally provision resources based on demand. In this article, we proposed a comprehensive taxonomy of workload scheduling and resource provisioning in hybrid cloud environments to investigate and classify 146 related research articles. Based on the investigation, we summarized the challenges which have not been addressed by these researches, and discussed future directions and trends in the area.

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Notes

  1. The objectives and constraints concerned by reviewed papers respectively correspond to the second and third columns in tables respectively summarizing related works for each type of workloads in “Appendix” which reviewed in detail each work by the categories classified by the workload type they focused on: the job with independent tasks, the workflow job, the (One-tier) web service as a whole, the (Multi-tier) web service with multiple components, etc.

  2. We use the Courier font to represent possible values for the properties of related works in tables in “Appendix”.

  3. The Resource Characteristics of the local and public clouds regarded by reviewed papers respectively correspond to the fourth and fifth columns in tables in “Appendix”.

  4. The concerned cost of local resources (Local Cost) in related works corresponds to the sixth column in the tables in “Appendix”.

  5. In the tables, the public resources concerned to be charged (charged), the Price Model and the Charge Unit for public VMs correspond to the seventh, eighth, and ninth columns, respectively, in “Appendix”.

  6. In “Appendix”, the last column of tables showed the factors of processing time concerned by related works.

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Acknowledgements

The research was supported in part by the Key Scientific Research Projects of Henan Higher School (Grant No. 19A520043), the Key Science and Technology Program of Henan Province (Grant Nos. 192102210291, 172102210540), the National Natural Science Foundation of China (Grant Nos. 61872043, 61975187), Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University (No. QXTCP B201904), the Fund of the Beijing Key Laboratory of Internet Culture and Digital Dissemination Research (Grant No. ICDDXN004), the Foundation Training Program for Young Key Teachers of Zhengzhou University of Light Industry, and the Research Fund for the Doctoral Program of Zhengzhou University of Light Industry.

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Authors and Affiliations

  1. Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Bo Wang, Changhai Wang, Jie Cao, Xiao Cui & Ling Zhang

  2. Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University, Beijing, 100101, China

    Ying Song

  3. Beijing Key Laboratory of Internet Culture and Digital Dissemination, Beijing Information Science and Technology University, Beijing, 100101, China

    Ying Song

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  1. Bo Wang
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Appendix

Appendix

See Tables 10, 11, 12, 13, 14, and 15.

Table 10 Workload scheduling and resource provisioning for jobs with independent tasks in hybrid cloud with one optimization objective
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Table 11 Workload scheduling and resource provisioning for jobs with independent tasks in hybrid cloud with multiple optimization objectives
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Table 12 Workload scheduling and resource provisioning for workflow jobs in hybrid cloud
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Table 13 Workload scheduling and resource provisioning for the one-tier web services in hybrid cloud
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Table 14 Workload scheduling and resource provisioning for multi-tier web services in hybrid cloud
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Table 15 Workload scheduling and resource provisioning for the infrastructure service delivery in hybrid cloud
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Wang, B., Wang, C., Song, Y. et al. A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds. Cluster Comput 23, 2809–2834 (2020). https://doi.org/10.1007/s10586-020-03048-8

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