UJPS: Urgent Job Priority Scheduling in Hadoop YARN

The rapidly increasing demand for big data processing has necessitated the development of advanced scheduling policies that can effectively accommodate urgent job requirements. This paper presents the Urgent Job Priority Scheduler (UJPS) for Hadoop YARN, aimed at handling urgent jobs efficiently in big data processing. UJPS uses an Aging model to cut waiting times and prevent job starvation, a Dynamic Priority model for urgency-based prioritization, and a Container Load model to boost data locality and efficiency. Tested on Hadoop with benchmark tasks, UJPS outperforms five advanced schedulers, lowering waiting times by up to 81.42% and reducing job runtime by 32.90%. It prioritizes urgent tasks while ensuring overall efficiency, offering benefits to organizations using Hadoop YARN for timely job execution.