Cloud Workload Characterization

Chapter
First Online:

Abstract

In this chapter, we describe various Cloud workloads and optimization issues from the points of view of various players involved in Cloud Computing. A comprehensive categorization of various types of diverse workloads is proposed, and nature of stress that each of these places on the resources in a data center is described. These categorizations extend beyond the Cloud for completeness. The Cloud Workload categories proposed in this chapter are: Big Streaming Data, Big Database Creation and Calculation, Big Database Search and Access, Big Data Storage, In-Memory Database, Many Tiny Tasks (Ants), High-Performance Computing (HPC), Highly Interactive Single-Person, Highly Interactive Multi-Person Jobs, Single-computer intensive jobs, Private Local Tasks, Slow Communication, Real-Time Local Tasks, Location-Aware Computing, Real-Time Geographically Dispersed, Access Control, and Voice or Video over IP. We evaluate causes of resource contention in a multi-tenanted data center and conclude by suggesting several remedial measures that both a Cloud Service provider and Cloud customers can undertake to minimize their pain points. This chapter identifies the relationship of critical computer resources to various workload categories. Low-level hardware measurements can be used to distinguish job transitions between categories and within phases of categories. This relationship with the categories allows a technical basis for SLAs, capital purchase decisions, and future computer architecture design decisions. A better understanding of these pain points, underlying causes, and suggested remedies will help IT managers to make intelligent decisions about moving their mission-critical or enterprise-class jobs into Public Clouds.

This is a preview of subscription content, log in to check access

References

  1. 1.
    Appleby K, Fakhouri S, Fong L, Goldszmidt G, Kalantar M, Krishnakumar S, Pazel DP, Pershing J, Rochwerger B (2001) Oceano-SLA based management of a computing utility. In: IEEE/IFIP international symposium on integrated network management proceedings, pp 855–868Google Scholar
  2. 2.
    Ardagna D, Trubian M, Zhang L (2007) SLA based resource allocation policies in autonomic environments. J Parallel Distrib Comput 67(3):259–270CrossRefMATHGoogle Scholar
  3. 3.
    Alarm S, Barrett RF, Kuehn JA, Roth PC, Vetter JS (2006) Characterization of scientific workloads on systems with multi-core processors. In: 2006 IEEE international symposium on workload characterization, pp 225–236Google Scholar
  4. 4.
    Ersoz D, Yousif MS, Das CR (2007) Characterizing network traffic in a cluster-based, multi-tier data center. In: Distributed computing systems, 2007 ICDCS’07. 27th international conference on 2007, p 59Google Scholar
  5. 5.
    Khan A, Yan X, Tao S, Anerousis N (2012) Workload characterization and prediction in the cloud: a multiple time series approach. In: Network operations and management symposium (NOMS), 2012 IEEE, pp 1287–1294Google Scholar
  6. 6.
    Bennani MN, Menasce DA (2005) Resource allocation for autonomic data centers using analytic performance models. In: Second international conference on proceedings on autonomic computing, ICAC 2005, pp 229–240Google Scholar
  7. 7.
    Bodnarchuk R, Bunt R (1991) A synthetic workload model for a distributed system file server. In: ACM SIGMETRICS performance evaluation review 1991, pp 50–59Google Scholar
  8. 8.
    Bienia C, Kumar S, Singh JP, Li K (2008) The PARSEC benchmark suite: characterization and architectural implications. In: Presented at the proceedings of the 17th international conference on parallel architectures and compilation techniques, Toronto, Ontario, Canada, 2008Google Scholar
  9. 9.
    Jackson KR, Ramakrishnan L, Muriki K, Canon S, Cholia S, Shalf J, Wasserman HJ, Wright NJ (2010) Performance analysis of high performance computing applications on the amazon web services Cloud. In: 2010 IEEE Second international conference on cloud computing technology and science (CloudCom), pp 159–168Google Scholar
  10. 10.
    Zhang Q, Hellerstein JL, Boutaba R (2011) Characterizing task usage shapes in Google’s compute clusters. In: Proceedings of Large-Scale Distributed Systems and Middleware (LADIS 2011)Google Scholar
  11. 11.
    Arlitt MF, Williamson CL (1997) Internet web servers: workload characterization and performance implications. IEEE/ACM Trans Network (ToN) 5(5):631–645CrossRefGoogle Scholar
  12. 12.
    Chesire M, Wolman A, Voelker G, Levy H (2001) Measurement and analysis of a streaming-media workload. In: Proceedings of the 2001 USENIX symposium on internet technologies and systemsGoogle Scholar
  13. 13.
    Maxiaguine A, Künzli S, Thiele L (2004) Workload characterization model for tasks with variable execution demand. In: Proceedings of the conference on design, automation and test in Europe, vol 2, p 21040Google Scholar
  14. 14.
    Yu PS, Chen MS, Heiss HU, Lee S (1992) On workload characterization of relational database environments. IEEE Trans Softw Eng 18:347–355CrossRefGoogle Scholar
  15. 15.
    Calzarossa M, Serazzi G (1985) A characterization of the variation in time of workload arrival patterns. IEEE Trans Comput 100:156–162CrossRefGoogle Scholar
  16. 16.
    Standard Performance Evaluation Corporation (2006) SPEC CPU2006. Available: http://www.spec.org/cpu2006/, 8 Nov 2013
  17. 17.
    Skinner D (2005) Integrated performance monitoring: a portable profiling infrastructure for parallel applications. In: Proceedings of ISC2005: international supercomputing conference, Heidelberg, GermanyGoogle Scholar
  18. 18.
    National Energy Research Scientific Computing Center (2013) NERSC. Available: www.nersc.gov, 8 Nov 2013
  19. 19.
    Xie Y, Loh G (2008) Dynamic classification of program memory behaviors in CMPs. The 2nd workshop on chip multiprocessor memory systems and interconnectsGoogle Scholar
  20. 20.
    Younggyun K, Knauerhase R, Brett P, Bowman M, Zhihua W, Pu C (2007). An analysis of performance interference effects in virtual environments. In: ISPASS 2007 IEEE international symposium on performance analysis of systems and software, pp 200–209Google Scholar
  21. 21.
    Khanna R, Kumar MJ (2011) A vision for platform autonomy. Publisher Intel PressGoogle Scholar
  22. 22.
    Chapman MRR (2006) In search of stupidity: over twenty years of high tech marketing disasters. Publisher ApressGoogle Scholar
  23. 23.
    Schneier B (2009) Schneier on security. Publisher WileyGoogle Scholar
  24. 24.
    Intel Corporation (2013) VTune Amplifier XE. Available: http://software.intel.com/en-us/intel-vtune-amplifier-xe, 8 Nov 2013
  25. 25.
    Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I (2010) A view of cloud computing. Commun ACM 53:50–58CrossRefGoogle Scholar
  26. 26.
    Bacigalupo DA, van Hemert J, Usmani A, Dillenberger DN, Wills GB, Jarvis SA (2010) Resource management of enterprise cloud systems using layered queuing and historical performance models. In IEEE international symposium on parallel & distributed processing, workshops and Ph.D. forum (IPDPSW), 2010, pp 1–8Google Scholar
  27. 27.
    Knauerhase R, Brett P, Hohlt B, Li T, Hahn S (2008) Using OS observations to improve performance in multicore systems. IEEE Micro 28:54–66CrossRefGoogle Scholar
  28. 28.
    Fedorova A, Blagodurov S, Zhuravlev S (2010) Managing contention for shared resources on multicore processors. Commun ACM 53:49–57CrossRefGoogle Scholar
  29. 29.
    Fedorova A, Seltzer M, Smith MD (2007) Improving performance isolation on chip multiprocessors via an operating system scheduler. In: Presented at the proceedings of the 16th international conference on parallel architecture and compilation techniquesGoogle Scholar
  30. 30.
    Nesbit KJ, Moreto M, Cazorla FJ, Ramirez A, Valero M, Smith JE (2008) Multicore resource management. IEEE Micro 28:6–16CrossRefGoogle Scholar
  31. 31.
    Intel Corporation (2013) Intel Data Center Manager(TM). Available: www.intel.com/DataCenterManager, 8 Nov 2013

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Naresh Kumar Sehgal
    • 1
  • Pramod Chandra P. Bhatt
    • 2
  1. 1.Santa ClaraUSA
  2. 2.BangaloreIndia

Personalised recommendations