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Availability-Aware Virtual Resource Provisioning for Infrastructure Service Agreements in the Cloud

Author

Listed:
  • Shuai Yuan

    (Brock University)

  • Sanjukta Das

    (University at Buffalo, State University of New York)

  • Ram Ramesh

    (University at Buffalo, State University of New York)

  • Chunming Qiao

    (University at Buffalo, State University of New York)

Abstract

Service availability is a key construct in Service Level Agreements (SLA) between a cloud service provider and a client. The provider typically allocates backup resources to mitigate the risk of violating the SLA-specified uptime guarantee. However, initial backups may need to be adjusted in response to real-time failure and recovery events. In this study, we first develop a recurrent intervention at fixed intervals (RIFI) strategy that allows the provider to adjust the allocation of backup resources such that the expected total cost is minimized. Next, we focus on the limit to number of interventions, starting from single intervention strategy, as frequent reallocations may be operationally disruptive. Particularly, we provide a cost minimization approach to guide the service providers in their virtual resources management, and a specific downtime minimization approach for more mission-critical applications as a more aggressive alternative. We present computational results exploring the impact of intervention on the likelihood of SLA violation for the rest of the contract period, and evaluate parameters such as time and quantum of resource level adjustment, penalty levels desired by clients, and their influences on the backup resource provisioning strategies. We also validate our models through the analysis of use cases from Amazon Elastic Compute Cloud. Finally, we summarize this study by providing key practical managerial implications for resource deployment in the availability-aware cloud.

Suggested Citation

  • Shuai Yuan & Sanjukta Das & Ram Ramesh & Chunming Qiao, 2023. "Availability-Aware Virtual Resource Provisioning for Infrastructure Service Agreements in the Cloud," Information Systems Frontiers, Springer, vol. 25(4), pages 1495-1512, August.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:4:d:10.1007_s10796-022-10302-4
    DOI: 10.1007/s10796-022-10302-4
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    References listed on IDEAS

    as
    1. Zhiling Guo & Jin Li & Ram Ramesh, 2019. "Optimal Management of Virtual Infrastructures Under Flexible Cloud Service Agreements," Information Systems Research, INFORMS, vol. 30(4), pages 1424-1446, April.
    2. Shuai Yuan & Sanjukta Das & R. Ramesh & Chunming Qiao, 2018. "Service Agreement Trifecta: Backup Resources, Price and Penalty in the Availability-Aware Cloud," Information Systems Research, INFORMS, vol. 29(4), pages 947-964, December.
    3. Jingqi Yang & Chuanchang Liu & Yanlei Shang & Bo Cheng & Zexiang Mao & Chunhong Liu & Lisha Niu & Junliang Chen, 2014. "A cost-aware auto-scaling approach using the workload prediction in service clouds," Information Systems Frontiers, Springer, vol. 16(1), pages 7-18, March.
    4. J. Octavio Gutierrez-Garcia & Kwang Mong Sim, 2012. "GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications," Information Systems Frontiers, Springer, vol. 14(4), pages 925-951, September.
    5. Mohammad Mehedi Hassan & M. Shamim Hossain & A. M. Jehad Sarkar & Eui-Nam Huh, 2014. "Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform," Information Systems Frontiers, Springer, vol. 16(4), pages 523-542, September.
    6. Benedikt Martens & Frank Teuteberg, 2012. "Decision-making in cloud computing environments: A cost and risk based approach," Information Systems Frontiers, Springer, vol. 14(4), pages 871-893, September.
    7. Sanjaya K. Panda & Indrajeet Gupta & Prasanta K. Jana, 2019. "Task scheduling algorithms for multi-cloud systems: allocation-aware approach," Information Systems Frontiers, Springer, vol. 21(2), pages 241-259, April.
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