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Prabhutendolkar, Prathamesh Dattatray (2022) Enhancing Load Balancing in Cloud Computing and Reducing Makespan by using Hybrid Particle Swarm Optimisation Algorithm to Improve Task Scheduling. Masters thesis, Dublin, National College of Ireland.
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Abstract
Utilizing virtual machines as the resource unit, cloud computing provides customers with a range of computational services through the Internet, including backups, software, databases, and servers. Tasks are distributed between virtual machines in a cloud computing ecosystem, each of which has a varied length, beginning, and processing time. Therefore, distributing these loads equally throughout the available cluster of virtual machines is a critical part. To achieve maximum utilization of the cluster’s capabilities and enhance system efficiency, task scheduling strategies must be implemented in a way that helps balance the workload among all VMs. Recently, researchers have introduced nature-inspired algorithms into the field of task scheduling to overcome challenges connected with complexity and to deliver optimum solutions. In this research paper, we present a unique loadbalancing method called the Hybrid Particle Swarm Optimization Algorithm to distribute workloads among the available cloud resources in such a way that reduces execution time and increases resource utilization. The research is focused on improving the existing task scheduling strategies to generate the best possible schedules for the inbound tasks. This is achieved by combining the Honey Badger algorithm with the already existing PSO algorithm. The digging and honey-finding phases of the Honey Badger algorithm aid PSO to evade the local optimum and find a superior solution in the available search space. The proposed task scheduling algorithm is implemented and evaluated using the Matlab simulation tool. The simulation results clearly outline that the proposed algorithm in this research is better in terms of reducing the makespan and increasing resource usage compared to several existing meta-heuristic optimization algorithms.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mijumbi, Rashid 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: | Tamara Malone |
| Date Deposited: | 19 Apr 2023 13:16 |
| Last Modified: | 19 Apr 2023 13:16 |
| URI: | https://norma.ncirl.ie/id/eprint/6484 |
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