A new priority rule cloud scheduling technique that utilizes gaps to increase the efficiency of jobs distribution

Abstract

In recent years, the concept of cloud computing has been gaining traction to provide dynamically increasing access to shared computing resources (software and hardware) via the internet. It’s no secret that cloud computing’s ability to supply mission-critical services has made job scheduling a hot subject in the industry right now. However, the efficient utilization of these cloud resources has been a challenge, often resulting in wastage or degraded service performance due to poor scheduling. To solve this issue, existing research has been focused on queue-based job scheduling techniques, where jobs are scheduled based on specific deadlines or job lengths. To overcome this challenge, numerous researchers have focused on improving existing Priority Rule (PR) cloud schedulers by developing dynamic scheduling algorithms, but they have fallen short of meeting user satisfaction, such as flowtime, makespan, and total tardiness. These are the limitations of the current implementation of existing Priority Rule (PR) schedulers, mainly caused by blocking made by jobs at the head of the queue. These limitations lead to the poor performance of cloud-based mobile applications and other cloud services. To address this issue, the main objective of this research is to improve the existing PR cloud schedulers by developing a new dynamic scheduling algorithm by manipulating the gaps in the cloud job schedule. In this thesis, first a Priority-Based Fair Scheduling (PBFS) algorithm has been introduced to schedule jobs so that jobs get access to the required resources at optimal times. Then, a backfilling strategy called Shortest Gap Priority-Based Fair Scheduling (SG-PBFS) is proposed that attempts to manipulate the gaps in the schedule of cloud jobs. Finally, the performance evaluation demonstrates that the proposed SG-PBFS algorithm outperforms SG-SJF, SG-LJF, SG-FCFS, SG-EDF, and SG-(MAX-MIN) in terms of flow time, makespan time, and total tardiness, which conclusively demonstrates its effectiveness. The experiment result shows that for 500 jobs, SG-PBFS flow time, makespan time, and tardiness time are 9%, 4%, and 7% less than PBFS gradually