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 E-mail:kj.ye@siat.ac.cn
  • 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.

PDF (PC)

424

Supplement

1

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;

[1] Lu C, Ye K, Xu G et al. Imbalance in the cloud:An analysis on Alibaba cluster trace. In Proc. the 2017 IEEE Int. Big Data, December 2017, pp.2884-2892.
[2] Panda S K, Jana P K. SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. The Journal of Supercomputing, 2017, 73(6):2730-2762.
[3] Hosseinimotlagh S, Khunjush F, Samadzadeh R. Seats:Smart energy-aware task scheduling in real-time cloud computing. The Journal of Supercomputing, 2015, 71(1):45-66.
[4] Shen Y, Bao Z, Qin X et al. Adaptive task scheduling strategy in cloud:When energy consumption meets performance guarantee. World Wide Web, 2017, 20(2):155-173.
[5] Gao W, Zhan J, Wang L et al. BigDataBench:A scalable and unified big data and AI benchmark suite. arXiv:1802.08254, 2018. https://arxiv.org/abs/1802.08254,November 2019.
[6] Ferdman M, Adileh A, Koçberber O et al. Clearing the clouds:A study of emerging scale-out workloads on modern hardware. ACM SIGPLAN Notices, 2012, 47(4):37-48.
[7] Jia Z, Zhan J, Wang L et al. Understanding big data analytics workloads on modern processors. IEEE Trans. Parallel and Distributed Systems, 2017, 28(6):1797-1810.
[8] Chen W, Ye K, Xu C. Co-locating online workload and offline workload in the cloud:An interference analysis. In Proc. the 21st Int. High Performance Computing and Communications, August 2019, pp.2278-2283.
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[3] Sun Zhongxiu; Shang Lujun;. DMODULA:A Distributed Programming Language[J]. , 1986, 1(2): 25 -31 .
[4] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[5] Wu Enhua;. A Graphics System Distributed across a Local Area Network[J]. , 1986, 1(3): 53 -64 .
[6] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[7] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[8] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[9] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[10] Zheng Guoliang; Li Hui;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

 Home
Editorial Board
Author Guidelines
Subscription		 Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved