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Energy-Aware Dynamic Virtual Machine Scheduling in Cloud Computing: A Survey

Published: 28 September 2023 Publication History

Abstract

Dynamic virtual machine scheduling is a crucial research area in cloud computing, to optimize resource utilization, reduce energy consumption, and ensuring performance and availability by dynamically managing and scheduling virtual machines (VMs) across servers. Effective dynamic VM scheduling can significantly impact the efficiency and sustainability of data centers by efficiently allocating resources based on workload demand. However, the dynamic nature of workload demand presents one of the primary challenges in dynamic VM scheduling. Sudden spikes or drops in server resource usage can lead to severe SLA violation concerns and energy inefficiency. To mitigate these challenges, scheduling algorithms need to balance the workload demand across servers while ensuring that servers are not overloaded or underloaded. In this work, we present a comprehensive comparison of state-of-the-art techniques for dynamic VM scheduling, evaluating their performance based on several key factors, including objective function, approach, the technique of VM scheduling, VM placement approach, VM selection approach, and server underload and overload detection techniques. Our comparison highlights the strengths and weaknesses of different approaches and provides insights into the design of efficient dynamic VM scheduling algorithms. The findings of this work can guide the development of novel techniques that address the challenges of dynamic VM scheduling and improve the efficiency and sustainability of data centers.

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IC3-2023: Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing
August 2023
783 pages
ISBN:9798400700224
DOI:10.1145/3607947
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 28 September 2023

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Author Tags

  1. Bin packing
  2. Cloud computing
  3. Cloud data center
  4. Energy-efficient VM scheduling
  5. Green computing
  6. Particle Swarm Optimization
  7. Power consumption
  8. Virtualization

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