Improving Task Scheduling and Resource Allocation by Optimizing Swarm Based Metaheuristic Algorithm with Evolutionary Based Optimizer

Bisht, Priya (2024) Improving Task Scheduling and Resource Allocation by Optimizing Swarm Based Metaheuristic Algorithm with Evolutionary Based Optimizer. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cloud computing has transformed traditional service deployment methods, allowing users to access various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) without investing in their own infrastructure. Task scheduling within these environments is critical for optimizing resource utilization, minimizing completion times, and ensuring cost-effectiveness. Scheduling tasks with varying complexities and resource requirements remains a significant challenge due to its NP-complete nature. This research addresses this challenge by proposing a hybridized approach that integrates swarm-based and evolutionary-based metaheuristic algorithms to enhance task scheduling efficiency. The research brings forward a combined modal which focuses on optimizing the Honey Badger Algorithm (swarm based) with Genetic Algorithm (evolutionary based) to improve task scheduling performance by reducing makespan time. this is compared to AntLion Optimizer and original HBA. With such a combination of global exploration capabilities of swarm based with refinement and exploitation strengths of evolutionary algorithms the proposed optimized algorithm tries to achieve better allocation of tasks and resource utilization with improved makespan time. This optimised algorithm was tested against the Ant Lion Optimizer and the original HBA through multiple scenarios with varying values of Probability ratio (PR) and VM and task counts. The analysis also implies the importance of right choice of algorithm based on the specific task requirements, considering the number of VM, tasks, and varying hyperparameters like PR in this case. Analysis results show that the proposed hybrid HBA-GA approach outperforms existing methods in terms of reducing makespan time and balancing workload distribution across resources thus increasing performance. This research contributes to the advancement of cloud computing task scheduling by presenting a novel hybrid algorithm that enhances scalability, reliability, and overall performance, addressing the limitations of traditional and standalone metaheuristic algorithms.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Lugones, Diego UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	03 Jul 2025 09:10
Last Modified:	03 Jul 2025 09:10
URI:	https://norma.ncirl.ie/id/eprint/8011

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