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JAIT 2025 Vol.16(2): 189-197
doi: 10.12720/jait.16.2.189-197

The Application of Virtual Machine Placement Using Fuzzy Grouping Genetic Algorithm

Jayesh Mohanrao Sarwade 1,*, Kapil Netaji Vhatkar 2, Shudhodhan Balbhim Bokefode 3,
Kishor Shamrao Sakure 3, and Sachin Chandusing Rathod 4
1. Department of Information Technology, JSPM’s Rajarshi Shahu College of Engineering, Pune, India
2. Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, India
3. Department of Computer Engineering, Terna Engineering College, Nerul, Navi Mumbai, India
4. Department of Information Technology, Sinhgad College of Engineering, Pune, India
Email: jayesh.sarwade@gmail.com (J.M.S.); kapilnv@gmail.com (K.N.V.); shudhodhanbokefode@ternaengg.ac.in (S.B.B.); kishorsakure@ternaengg.ac.in (K.S.S.); rathodsach@gmail.com (S.C.R.)
*Corresponding author

Manuscript received June 8, 2024; revised July 5, 2024; accepted August 6, 2024; published February 10, 2025.

Abstract—The widespread use of cloud computing has caused a rapid increase in data center electricity energy costs. An important process in cloud computing is Virtual Machine Placement (VMP), which analyses the most suitable Physical Machine (PM) to host the VMs. The challenge for cloud providers has been determining the best location for VMs in data centers to provide optimal performance and high availability. Efficient utilization of the cloud’s multi-dimensional resources is vital in addressing VMP concerns and minimizing energy consumption to avoid resource wastage. The objective of this multi-objective VMP issue is to minimize the rate of Service-Level Agreement (SLA) violations, power consumption, and resource usage. To improve the genetic algorithm’s fitness calculation and solve this important issue, a Fuzzy Grouping Genetic Algorithm (FGGA) with fuzzy membership is used. The applied algorithm’s fitness function is calculated using a fuzzy membership-based function that considers numerous optimization targets that may have differing degrees of influence on the problem. In comparison to other existing methods including Utilization Based Genetic Algorithm (UBGA), Requirement Not Met Selection (RNMS), and Improved Genetic Algorithm (I-GA), the implemented method achieved reduced energy consumption, communication cost, resource wastage, and overall VM run time. Compare to RNMS, implemented FGGA attained less VM execution time, and energy consumption values are 1.6×107 ms, and 8×108 KWh. While compared to I-GA, implemented FGGA obtained lower resource wastage performance and communication cost performance values are 0.18 and 1000 at 1000 VMs.
 
Keywords—cloud computing, Fuzzy Grouping Genetic Algorithm (FGGA), Physical Machine (PM), Virtual Machine (VM), Virtual Machine Placement (VMP)

Cite: Jayesh Mohanrao Sarwade, Kapil Netaji Vhatkar, Shudhodhan Balbhim Bokefode, Kishor Shamrao Sakure, and Sachin Chandusing Rathod, "The Application of Virtual Machine Placement Using Fuzzy Grouping Genetic Algorithm," Journal of Advances in Information Technology, Vol. 16, No. 2, pp. 189-197, 2025. doi: 10.12720/jait.16.2.189-197

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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