Skip to main content
Log in

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

  • Short Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ke-Jiang Ye.

Electronic supplementary material

ESM 1

(PDF 478 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, WY., Ye, KJ., Lu, CZ. et al. Interference Analysis of Co-Located Container Workloads: A Perspective from Hardware Performance Counters. J. Comput. Sci. Technol. 35, 412–417 (2020). https://doi.org/10.1007/s11390-020-9707-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-020-9707-y

Keywords

Navigation