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Gandhi Raja, Shiva Ram Raja (2023) Energy Efficient Task Scheduling Approach in Cloud Environments towards Green Cloud Computing. Masters thesis, Dublin, National College of Ireland.
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Abstract
Assigning and distributing computing resources in an environment of the cloud is known as task allocation or scheduling. Despite the efficiency and effectiveness in an environment of cloud computing, task allocation or scheduling is still a challenge for the same. While task scheduling is responsible for the distribution of load evenly during the mapping of resources, there are multiple challenges faced during the same. While there have been multiple studies on cloud computing and task scheduling, the existing studies have less focus on the effective utilization of resources that is compute, storage, and network capacities. And therefore, failed to focus on the implementation of task scheduling algorithms. The goal of the current thesis is to concentrate on a hybrid GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) which is a GA-PSO work scheduling technique. This GAPSO scheduling technique efficiently distributes the jobs across the resources. The suggested technique incorporates characteristics of modified GA-PSO algorithms to shorten the makespan, shorten the execution and turnaround time, and to decrease the communication and execution cost. Using the CloudSim framework, the efficiency of the GA-PSO approach will be compared to that of the conventional PSO algorithm. The results show that by conserving time, money, and resources, the Hybrid GA-PSO technique speeds up the convergence to an ideal solution. The proposed technique has overall enhancement and its performance in terms of execution cost as well as makespan.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mijumbi, Rashid UNSPECIFIED |
| Subjects: | T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms H Social Sciences > HC Economic History and Conditions > Natural resources > Power resources > Energy consumption |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Tamara Malone |
| Date Deposited: | 20 Aug 2024 16:23 |
| Last Modified: | 20 Aug 2024 16:23 |
| URI: | https://norma.ncirl.ie/id/eprint/7051 |
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