Efficient Load Balancing and Optimal Resource Allocation Using Max-Min Heuristic Approach and Enhanced Ant Colony Optimization Algorithm over Cloud Computing

Authors

  • M. R. Banupriya Associate Professor, Department of Computer Applications, Kongunadu Arts and Science College, Coimbatore.
  • D. Francis Xavier Christopher Principal & Professor in Computer Science, SRM Trichy Arts and Science College, Trichy

Keywords:

Cloud computing, Max-Min Heuristic (MMH) and Enhanced Ant Colony Optimization (EACO) algorithm, load balancing, resource allocation

Abstract

The paradigm of virtualization technology, which underpins cloud computing, has lately become one of the most prominent concepts in the information technology (IT) sector. Virtualization is a technology which helps the users to access the cloud services. In the existing system, the resource allocation is not ensured and in few cases, speed of the process is reduced due to convergence issues. Hence, the performance of cloud computing as a whole has greatly declined. The Max-Min Heuristic (MMH) and Enhanced Ant Colony Optimisation (EACO) algorithms are introduced in this study to enhance load balancing and optimum resource allocation on the cloud to address the aforementioned difficulties. The suggested system comprises four primary stages, including cost-effective Virtual Machine (VM) migration, load balancing, and resource allocation. First, think about how many resources, tasks, virtual machines, and cloud users there are in cloud computing. This study uses the MMH method for load balancing, which equalizes the overall workloads throughout the cloud. By moving tasks from overloaded nodes to under loaded nodes, load balancing is accomplished. Following that, the EACO algorithm is utilized to allocate resources in a way that effectively chooses more optimum resources. In order to effectively fulfil Quality of Service (QoS) standards, it is utilized to choose the best resources for the relevant cloud needs. Increasing throughput and VM performance in the cloud, as well as lowering costs, are other key objectives. Finally, a cost-effective VM migration technique is used, which is based on the Weighted Support Vector Machine (WSVM) algorithm. With the use of SVM weight values, it is designed to identify the pattern of overload and underload. It also finds VM migration strategies that consume the least amount of energy while maintaining high service standards. The simulation results show that, compared to the current approaches, the proposed MMH+EACO algorithm performs better thanks to increased throughput and reduced computational complexity, cost complexity, Mean Square Error (MSE) rate, and energy usage.

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Published

02.09.2023

How to Cite

Banupriya, M. R. ., & Xavier Christopher, D. F. . (2023). Efficient Load Balancing and Optimal Resource Allocation Using Max-Min Heuristic Approach and Enhanced Ant Colony Optimization Algorithm over Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 258–270. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3413

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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