Optimizing Load Balancing in cloud computing using Enhanced Firefly Algorithm (EFA) Method

Shah, Jayant Umesh (2024) Optimizing Load Balancing in cloud computing using Enhanced Firefly Algorithm (EFA) Method. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cloud computing has seen a surge in interest from both the academic and business sectors due to its scalability and virtualization benefits. Efficient load balancing within cloud data centers is essential to maintain system stability and performance. Load Balancing (LB) is particularly critical in cloud environments where providers serve a multitude of clients, necessitating robust task scheduling that ensures equitable load distribution. Various strategies and algorithms have been developed to advance LB in cloud services, focusing on minimizing task execution time, reducing energy consumption, optimizing resource use, and rapidly allocating tasks among clusters of Virtual Machines (VMs). Nonetheless, these solutions often overlook the existing load on VMs, potentially leading to overload issues. To address this, the research introduces the Enhanced Firefly Algorithm (EFA), a refined approach leveraging Metaheuristic Optimization. EFA intelligently evaluates VM workloads in cloud infrastructures to distribute tasks without overloading any single VM. It is compared against traditional LB algorithms like Round Robin Algorithm (RRA) and Ant Colony Algorithm (ACO), using metrics such as resource consumption, computational speed, and time efficiency. The results demonstrate EFA’s superior performance over RRA and ACO, particularly as the number of task units (cloudlets) increases. EFA achieves better execution times, enhanced resource utilization, and more efficient LB solutions. This study underscores the necessity for LB mechanisms that consider the current load of VMs alongside resource, timing, and energy metrics, presenting EFA as a viable solution for dynamic and efficient load distribution in cloud computing.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kazmi, Aqeel UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	03 Jun 2025 12:41
Last Modified:	03 Jun 2025 12:41
URI:	https://norma.ncirl.ie/id/eprint/7723

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