K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications. Issue 2 (22nd January 2020)

• Record Type: Journal Article
• Authors: Liu, Bo; Li, Jiawei; Lin, Weiwei; Bai, Weihua; Li, Pengfei; Gao, Qian
• Other Names: Meng Weizhi guestEditor.; Cofta Piotr guestEditor.; Grandison Tyrone W.A. guestEditor.
• Is Part Of: International journal of network management. Volume 31:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-22
• Subjects: Computer networks -- Management -- Periodicals; 004.6
• DOI: 10.1002/nem.2092
• Languages: English
• ISSNs: 1055-7148

Abstract

Summary: In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes‐based particle swarm optimization(K‐PSO). Experimental results show that the proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K‐PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of the Kube‐scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded. Abstract : The proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of Kube‐Scheduler default strategy, Balanced Qos Priority strategy, ESS strategy, and PSO strategy while the average I/O performance and average computing performance of Hadoop cluster are not degraded.