Multiobjective Dynamic Resource Allocation in Cloud Computing using Harris Hawk Optimization Algorithm (MDLB-HHO)

Multiobjective Dynamic Resource Allocation in Cloud Computing using Harris Hawk Optimization Algorithm (MDLB-HHO) | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 7 March 2025 V1 Latest version Share on Multiobjective Dynamic Resource Allocation in Cloud Computing using Harris Hawk Optimization Algorithm (MDLB-HHO) Authors : C.M. Varun [email protected] and R.P. Anto Kumar Authors Info & Affiliations https://doi.org/10.22541/au.174134718.82491518/v1 162 views 99 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Effective load balancing and resource distribution strategies are essential for optimizing performance and resource usage in cloud computing. Cloud computing necessitates flexible, dynamic load balancing and resource allocation among multiple goals. Load balancing and resource allocation in cloud computing are complex tasks. The primary goal of dynamic load balancing in cloud computing systems is to enhance resource usage and improve the efficiency of task allocation. The HHO algorithm is accountable for the dynamic allocation of tasks to virtual machines (VMs) according to the distribution of workload and usage of resources. Multiple experimental evaluations and comparisons with alternative load-balancing approaches have proved that the HHO algorithm successfully and efficiently manages dynamic load-balancing. These technological advancements have led to improved reaction times and enhanced resource efficiency. This approach offers a viable and efficient alternative for tackling load-balancing challenges in dynamic scenarios using the cooperative foraging behavior observed in hawks. The proposed approach accounts for the ever-changing demands of cloud applications and dynamically modifies the resource allocation technique. To accomplish this, a multiobjective fitness function is used to cut down on response time and overuse of resources while simultaneously improving resource efficiency. These findings suggest it may help make cloud-based services more efficient and sustainable. The Harris Hawks perform an extensive review of the solution space, wherein they identify the optimal approach for task allocation and adapt to the dynamic workload conditions by employing a process characterized by iterative interactions and positional updates. This approach uses hawks’ collaborative search behavior to dynamically assign tasks to VMs while accounting for load balancing and resource utilization. The proposed methodology can adapt to ever-changing workload demand situations. It employs a multiobjective fitness function to effectively improve key performance indicators such as response time, resource utilization, and efficiency. This work demonstrates how the HHO algorithm could improve the effectiveness and longevity of cloud-based services under changing conditions. Supplementary Material File (mdlb-hho-author.docx) Download 666.57 KB Information & Authors Information Version history V1 Version 1 07 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cloud computing harris hawk optimization job scheduling load balancing Authors Affiliations C.M. Varun [email protected] RMK Engineering College View all articles by this author R.P. Anto Kumar St Xavier's Catholic College of Engineering View all articles by this author Metrics & Citations Metrics Article Usage 162 views 99 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation C.M. Varun, R.P. Anto Kumar. Multiobjective Dynamic Resource Allocation in Cloud Computing using Harris Hawk Optimization Algorithm (MDLB-HHO). Authorea . 07 March 2025. DOI: https://doi.org/10.22541/au.174134718.82491518/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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