Integrating Particle Swarm Optimization with Cloud Computing Task Scheduling for Optimal Resource Utilization
Keywords:
Particle Swarm Optimization, Cloud Computing, Task Scheduling, Resource Utilization, Energy Efficiency, Makespan, Average Waiting TimeAbstract
Therefore, the purpose of this research paper is to investigate the incorporation of PSO into schedule task on cloud computing systems. Generally, conventional techniques of scheduling encounter inefficiencies that inflict lighting energy waste and augment energy expenses. This paper outlines a novel, PSO-based scheduling algorithm that uses real feedback from current context to properly assign resources for tasks and improve metrics in terms of task time requirement, resource consumption, energy saving, makespan, and average task waiting time. Polynomial and numerical analysis of the proposed real time PSO- based solution applied to a simulated Cloud environment reveals that it is far superior to conventional methods such as FCFS and Round Robin. Thus, the results prove that the proposed PSO algorithm can be deemed a relatively strong solution of the problem of effective distribution of tasks in cloud computing environments while also providing the schedule that is responsive to the fluctuations in the work load.
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