An Reinforced PSO Algorithm With SJF-MMBF For Allocating Work In Cloud Computing

The current state of service provision involves a high amount of users, directing to the amount of requests directed towards cloud providers. In order to accommodate these demands, scalable techniques are proposed for service delivery. This paper delves into issues related to VM scheduling optimization within cloud systems that pertain mainly to resource allocation which includes CPU processing power, memory and storage facilities available at hand from a shared resources pool. Cloud computing turned consumers VMs as service providing on-demand access through random demand patterns while hosting duration widely differs among various using parties involved therein. Several algorithmic approaches have been proffered with SJF and FCFS algorithms being some examples; however this study seeks out how best we can design superior structural alterations specifically observed via Shortest-Job-First Scheduling Algorithm by introducing PSO alongside Multi-queue Schedule methodology improvement pursuits. Consequently evaluating this proposal requires examination under two main programming schemes: SJF buffering along MMBF schedules showing work hunger prevention measures modeled specific cases suited precisely under our unique hybrid-innovative SJF-MMFB system designed herein Meanwhile priority fixed schedules still entail proper planning programmatically when deploying such queue models constantly used too prevent starvation incidents effectively.