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Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Author

Listed:
  • Anna Kushchazli

    (Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia)

  • Anastasia Safargalieva

    (Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia)

  • Irina Kochetkova

    (Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
    Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., 119333 Moscow, Russia)

  • Andrey Gorshenin

    (Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., 119333 Moscow, Russia)

Abstract

The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.

Suggested Citation

  • Anna Kushchazli & Anastasia Safargalieva & Irina Kochetkova & Andrey Gorshenin, 2024. "Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing," Mathematics, MDPI, vol. 12(3), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:468-:d:1331524
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    References listed on IDEAS

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    1. Zahra Movahedi Nia & Mohammad Reza Khayyambashi & Ali Miri, 2020. "A static VM placement and hybrid job scheduling model for green data centers," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
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