Improved heuristic job scheduling method to enhance throughput for big data analytics

Data-parallel computing platforms, such as Hadoop and Spark, are deployed in computing clusters for big data analytics. There is a general tendency that multiple users share the same computing cluster. The schedule of multiple jobs becomes a serious challenge. Over a long period in the past, the Shortest-Job-First (SJF) method has been considered as the optimal solution to minimize the average job completion time. However, the SJF method leads to a low system throughput in the case where a small number of short jobs consume a large amount of resources. This factor prolongs the average job completion time. We propose an improved heuristic job scheduling method, called the Densest-Job-Set-First (DJSF) method. The DJSF method schedules jobs by maximizing the number of completed jobs per unit time, aiming to decrease the average Job Completion Time (JCT) and improve the system throughput. We perform extensive simulations based on Google cluster data. Compared with the SJF method, the DJSF method decreases the average JCT by 23.19% and enhances the system throughput by 42.19%. Compared with Tetris, the job packing method improves the job completion efficiency by 55.4%, so that the computing platforms complete more jobs in a short time span.