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Mohammed, Zain Ull Abbdin (2023) Enhanced Genetic Algorithm For Dynamic Dependent Workloads To Improve Load Balancing Efficiency in Cloud Computing. Masters thesis, Dublin, National College of Ireland.
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Abstract
In the recent years, the potential of cloud computing (CC) has attracted a lot of attention to improve the scalability of cloud Data Centers (DC). Effective load balancing (LB) solutions are required to ensure the best possible distribution of workload. LB is a major issue for distributed computing systems, particularly in cloud environments with several customers. To enhance LB in cloud computing, numerous tactics, methods, and techniques have been developed over time. These methods concentrate on lowering execution time, cutting down on energy usage, maximizing resource utilization, and accelerating task scheduling among a group of virtual machines (VMs). However, these traditional methods often fail to consider the dynamic and interdependent workload, which may result in overloading-related problems. An algorithm that can improve load balancing efficiency in cloud computing and dynamically manage dependent workloads is necessary to address this challenge. This research proposes an Enhanced Genetic Algorithm (EGA), which is based on natural process. It is a technique where two fittest parents (or virtual machines) are selected from the initial population (Datacenter, Virtual Machine, Cloudlets) and mutated to create an offspring (a new virtual machine) to manage the dynamic load. The outputs like total energy utilized, resource utilization, and execution time were captured in several iterations and the same was compared with Particle Swarm Optimization (a load balancing algorithm). The average results show that EGA outperforms PSO and is about 77% more efficient.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Sahni, Vikas 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 |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 09 Apr 2025 11:39 |
| Last Modified: | 09 Apr 2025 11:39 |
| URI: | https://norma.ncirl.ie/id/eprint/7393 |
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