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Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing

K. Naveen Durai*, R. Subha, Anandakumar Haldorai

Sri Eshwar College of Engineering, Coimbatore, 641202, India

* Corresponding Author: K. Naveen Durai. Email: email

Intelligent Automation & Soft Computing 2022, 34(1), 467-483.


In cloud computing, the processes of load balancing and task scheduling are major concerns as they are the primary mechanisms responsible for executing tasks by allocating and utilizing the resources of Virtual Machines (VMs) in a more optimal way. This problem of balancing loads and scheduling tasks in the cloud computing scenario can be categorized as an NP-hard problem. This problem of load balancing needs to be efficiently allocated tasks to VMs and sustain the trade-off among the complete set of VMs. It also needs to maintain equilibrium among VMs with the objective of maximizing throughput with a minimized time span. In this paper, a Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm-based-efficient Load Balancing (HIWIGOA-LB) technique is proposed by adopting the merits of the Invasive Weed Optimization Algorithm (IWOA) into the Grasshopper Optimization Algorithm (GOA) for determining the near-optimal solution that facilitates optimal load balancing. In particular, the random walk strategy is adopted to prevent the local point of optimality problem. It also utilized the strategy of grouping to modify the exploitation coefficient associated with the traditional GOA for balancing the rate of exploration and exploitation. The simulation investigations of the proposed HIWIGOA-LB scheme confirmed its better performance in minimizing the make span and response time by 13.21% and 16.71%, with a maximized throughput of 19.28%, better than the baseline approaches considered for investigation.


Cite This Article

K. Naveen Durai, R. Subha and A. Haldorai, "Hybrid invasive weed improved grasshopper optimization algorithm for cloud load balancing," Intelligent Automation & Soft Computing, vol. 34, no.1, pp. 467–483, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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