Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 412-417.doi: 10.1007/s11390-020-9707-y

• Special Section of ChinaSys 2019 • Previous Articles     Next Articles

Interference Analysis of Co-Located Container Workloads: A Perspective from Hardware Performance Counters

Wen-Yan Chen1, Ke-Jiang Ye1,*, Member, CCF, Cheng-Zhi Lu1, Dong-Dai Zhou2, Member, CCF, Cheng-Zhong Xu3, Fellow, IEEE        

  1. 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    2 School of Information Science and Technology, Northeast Normal University, Changchun 130117, China;
    3 Faculty of Science and Technology, University of Macau, Macau 999078, China
  • Received:2019-05-10 Revised:2020-02-06 Online:2020-03-05 Published:2020-03-18
  • Contact: Ke-Jiang Ye
  • About author:Wen-Yan Chen received her M.Eng. degree from Northeast Normal University, Changchun, in 2019, and B.Eng. degree from Zhengzhou University, Zhengzhou, in 2016, both in software engineering. She is currently a visiting student at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen. Her research interests focus on cloud computing.
  • Supported by:
    This work is supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004804, the National Natural Science Foundation of China under Grant No. 61702492, the Shenzhen Basic Research Program under Grant Nos. JCYJ20170818153016513 and JCYJ20170307164747920, and Alibaba Innovative Research (AIR) Project.

Workload characterization is critical for resource management and scheduling. Recently, with the fast development of container technique, more and more cloud service providers like Google and Alibaba adopt containers to provide cloud services, due to the low overheads. However, the characteristics of co-located diverse services (e.g., interactive on-line services, off-line computing services) running in containers are still not clear. In this paper, we present a comprehensive analysis of the characteristics of co-located workloads running in containers on the same server from the perspective of hardware events. Our study quantifies and reveals the system behavior from the micro-architecture level when workloads are running in different co-location patterns. Through the analysis of typical hardware events, we provide recommended/unrecommended co-location workload patterns which provide valuable deployment suggestions for datacenter administrators.

Key words: workload characterization; container; co-location pattern; hardware performance counter;

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