Priority Based Fair Scheduling: Enhancing Efficiency in Cloud Job Distribution

In recent years, there has been a growing interest in cloud computing as a means to enhance user access to shared computing resources, including software and hardware, through the internet. However, the efficient utilization of these cloud resources has been a challenge, often resulting in wastage or degraded service performance due to inadequate scheduling. 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. In this study, we introduce a new PR scheduler called Priority Based Fair Scheduling (PBFS), which takes into account key parameters such as CPU Time, Job Arrival Time, and Job Length. We evaluate the performance of PBFS by comparing it with five existing algorithms, and the results demonstrate that PBFS surpasses the performance of the other algorithms. The experiment was conducted using the CloudSim simulator, utilizing a dataset of 300 and 400 jobs. In order to assess the performance, three key metrics were employed: flow time, makespan time, and total tardiness. These metrics were chosen to evaluate and analyze the effectiveness of the proposed scheduling algorithm.