Improving Load Balancing in Cloud Computing to Minimize Response time and enhancing resource utilization by using Hybrid metaheuristic Ant Colony Optimization-Simulated Annealing Algorithm

Kumar, Anoop (2024) Improving Load Balancing in Cloud Computing to Minimize Response time and enhancing resource utilization by using Hybrid metaheuristic Ant Colony Optimization-Simulated Annealing Algorithm. Masters thesis, Dublin, National College of Ireland.

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Abstract

In cloud computing load balancing and task scheduling is critical and is used for the efficient distribution of workloads and computing resources among virtual machines. Load balancing is an NP-hard optimization problem. Unequal distribution of tasks among can lead to underloaded or overloaded VMs leading to poor resource utilization This research explores a hybrid meta-heuristic algorithm for task distribution and load balancing named ACO-SA, which combines the Ant colony optimization for exploring the solution search space and the Simulated annealing algorithm for Exploiting the search space and refining the solution. ACO is a bio-inspired meta-heuristic algorithm and mimics the foraging behavior of ants to find the shortest past to food representing the best way to distribute tasks among VMs. On other hand Simulated annealing algorithm is used to refine the search space for solutions and converge towards a near-optimal solution. The objective function(fitness function) is defined to minimize response time in ACO and SA. Response time and resource utilization is considered as evaluation parameters. The simulation was performed using the Cloudsim toolkit. The simulation results of the proposed ACO-SA algorithm showed improved performance in minimizing response time by 7% compared to traditional meta-heuristic algorithms such as ant colony optimization(ACO) and Particle swarm optimization(PSO). The hybrid algorithms combines the strength of ACO with SA to optimize load balancing in cloud computing.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Heeney, Sean UNSPECIFIED
Uncontrolled Keywords:	Cloud Computing; Task scheduling; Load Balancing; Ant Colony Optimization; Simulated annealing algorithm; Objective function; Response time; Resource Utilization
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 13:22
Last Modified:	03 Jul 2025 13:22
URI:	https://norma.ncirl.ie/id/eprint/8030

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