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K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications
International Journal of Network Management ( IF 1.5 ) Pub Date : 2020-01-22 , DOI: 10.1002/nem.2092 Bo Liu 1 , Jiawei Li 1 , Weiwei Lin 2 , Weihua Bai 3 , Pengfei Li 1 , Qian Gao 1
International Journal of Network Management ( IF 1.5 ) Pub Date : 2020-01-22 , DOI: 10.1002/nem.2092 Bo Liu 1 , Jiawei Li 1 , Weiwei Lin 2 , Weihua Bai 3 , Pengfei Li 1 , Qian Gao 1
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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.
中文翻译：
K‐PSO：针对大数据应用程序的基于PSO的改进的容器调度算法
近年来，Docker容器技术正以爆炸性的速度应用于云计算领域。Docker容器资源的调度已逐渐成为研究热点。现有的大数据计算和存储平台适用于传统的虚拟机技术，这通常会导致资源利用率低，需要很长时间才能灵活地扩展和扩展集群。在本文中，我们针对大数据应用提出了一种改进的容器调度算法，称为基于Kubernetes的粒子群优化（K-PSO）。实验结果表明，所提出的K‐PSO算法收敛速度快于基本PSO算法，并且算法的运行时间缩短了一半左右。在Kubernetes容器云系统中实现了K-PSO容器调度算法和针对大数据应用的算法实验。我们的实验结果表明，基于K‐PSO算法的改进调度策略的节点资源利用率比Kube‐Scheduler默认策略，平衡QoS优先级策略，ESS策略和PSO策略的节点资源利用率高约20％。 Hadoop群集的平均I / O性能和平均计算性能不会降低。
更新日期：2020-01-22
中文翻译：
K‐PSO：针对大数据应用程序的基于PSO的改进的容器调度算法
近年来，Docker容器技术正以爆炸性的速度应用于云计算领域。Docker容器资源的调度已逐渐成为研究热点。现有的大数据计算和存储平台适用于传统的虚拟机技术，这通常会导致资源利用率低，需要很长时间才能灵活地扩展和扩展集群。在本文中，我们针对大数据应用提出了一种改进的容器调度算法，称为基于Kubernetes的粒子群优化（K-PSO）。实验结果表明，所提出的K‐PSO算法收敛速度快于基本PSO算法，并且算法的运行时间缩短了一半左右。在Kubernetes容器云系统中实现了K-PSO容器调度算法和针对大数据应用的算法实验。我们的实验结果表明，基于K‐PSO算法的改进调度策略的节点资源利用率比Kube‐Scheduler默认策略，平衡QoS优先级策略，ESS策略和PSO策略的节点资源利用率高约20％。 Hadoop群集的平均I / O性能和平均计算性能不会降低。
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