TY - GEN
T1 - Intelligent Scheduling for Parallel Jobs in Big Data Processing Systems
AU - Xu, Mingrui
AU - Wu, Chase Q.
AU - Hou, Aiqin
AU - Wang, Yongqiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/8
Y1 - 2019/4/8
N2 - The explosive growth of data in various scientific, industrial, and business domains necessitates the use of big data processing systems, such as Hadoop, 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 KMTS. 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.
AB - The explosive growth of data in various scientific, industrial, and business domains necessitates the use of big data processing systems, such as Hadoop, 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 KMTS. 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.
KW - Task scheduling
KW - big data platform
KW - cluster manager
KW - heterogeneous clusters
UR - http://www.scopus.com/inward/record.url?scp=85064975553&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064975553&partnerID=8YFLogxK
U2 - 10.1109/ICCNC.2019.8685520
DO - 10.1109/ICCNC.2019.8685520
M3 - Conference contribution
AN - SCOPUS:85064975553
T3 - 2019 International Conference on Computing, Networking and Communications, ICNC 2019
SP - 22
EP - 28
BT - 2019 International Conference on Computing, Networking and Communications, ICNC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computing, Networking and Communications, ICNC 2019
Y2 - 18 February 2019 through 21 February 2019
ER -