Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lahande, Prathamesh Vijay; Kaveri, Parag Ravikant; Saini, Jatinderkumar R. Title A name given to the resource Novel Hybrid Machine-Learning Algorithms for Resource Optimization in Cloud Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1196 LNNS;pp.471-478 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9526-0_40" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9526-0_40</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009906960?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009906960?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lahande P.V., CHRIST University, Bangalore, India; Kaveri P.R., Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India; Saini J.R., Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India Description An account of the resource The resource optimization process in the cloud is crucial and can be achieved through the ideal Load Balancing (LB) mechanism. The cloud undergoes several challenges with resource optimization due to poor LB mechanism, where its Virtual Machines (VMs) are either overloaded or idle. The main aim of this experimental-based research is to enhance the LB mechanism of the cloud by implementing and comparing the performance of novel hybrid LB algorithms RLFCFS and RLSJF to optimize the resources. The RLFCFS and RLSJF novel LB algorithms are designed by combining the Reinforcement Learning (RL) technique with the heuristic FCFS and SJF algorithms. The proposed algorithms improve resource optimization in terms of cost and time by facilitating enhanced LB mechanism through RL intelligence mechanism. The performance of RLFCFS and RLSJF LB algorithms is compared with respect to the average (avg.) load managed by the VMs and the avg. percentage (perc.) of deviation observed against the expected load in each experimental stage. The experimental throughput conveys that the RLFCFS LB algorithm managed an aggregate avg. load of 968.77 tasks against the RLSJF LB algorithm, which managed 999.08 tasks aggregately across all experimental stages. Concerning the avg. perc. of deviation, the RLFCFS LB algorithm deviated by 63.44% against the ideal expected load to manage against the RLSJF LB algorithm, which deviated by 64.60%. This shows that the RLFCFS LB algorithm gave better resource optimization results than the RLSJF LB algorithm. Lastly, these results are mathematically validated using the Simple Linear Regression model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cloud; Load-balancing; Machine learning; Performance; Resource-optimization Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981979525-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online