Intelligent Scheduling for Parallel Jobs in Big Data Processing Systems

The explosive growth of data in various scientific, industrial, and business domains necessitates the use of big data processing systems, such as Iladoop, which are typically deployed in a physical or cloud-based cluster shared by many users running parallel jobs. As the user population and application scale increase, such systems are expanded from time to time with an addition of new nodes of different types, making the cluster highly heterogeneous. Job scheduling in such systems largely determines the performance of big data applications and remains to be a challenging problem. In this paper, we formulate a generic job scheduling problem for parallel processing of big data in heterogeneous clusters and design a k-means based task scheduling algorithm, referred to as KNITS. Simulation results show that KMTS improves execution performance by 25% and 30% on average in single job scheduling and parallel job scheduling, respectively, over existing methods. The performance superiority is also confirmed by real experiments in high-performance computing environments.