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Adaptive virtual machine migration based on performance-to-power ratio in fog-enabled cloud data centers

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Abstract

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement’s performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications ”E-PRWA” in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing.

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Authors and Affiliations

  1. Computer Science Department, College of Science, University of Sulaimani, Sulaimani, KRG, Iraq

    Mustafa I. Khaleel

  2. Computer Science Department, College of Science and Mathematics, Montclair State University, Montclair, NJ, USA

    Michelle M. Zhu

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  1. Mustafa I. Khaleel
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  2. Michelle M. Zhu
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Correspondence to Mustafa I. Khaleel.

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Khaleel, M.I., Zhu, M.M. Adaptive virtual machine migration based on performance-to-power ratio in fog-enabled cloud data centers. J Supercomput 77, 11986–12025 (2021). https://doi.org/10.1007/s11227-021-03753-0

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