Measuring Software Systems Scalability for Proactive Data Center Management

  • Nuno A. Carvalho
  • José Pereira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6427)

Abstract

The current trend of increasingly larger Web-based applications makes scalability the key challenge when developing, deploying, and maintaining data centers. At the same time, the migration to the cloud computing paradigm means that each data center hosts an increasingly complex mix of applications, from multiple owners and in constant evolution. Unfortunately, managing such data centers in a cost-effective manner requires that the scalability properties of the hosted workloads to be accurately known, namely, to proactively provision adequate resources and to plan the most economical placement of applications. Obviously, stopping each of them and running a custom benchmark to asses its scalability properties is not an option. In this paper we address this challenge with a tool to measure the software scalability regarding CPU availability, to predict system behavior in face of varying resources and an increasing workload. This tool does not depend on a particular application and relies only on Linux’s SystemTap probing infrastructure. We validate the approach first using simulation and then in an actual system. The resulting better prediction of scalability properties should allow improved (self-)management practices.

Keywords

Scalability Self-management Monitoring Provisioning 

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References

  1. 1.
    Middleware: ACM/IFIP/USENIX 9th International Middleware Conference (2008), http://middleware2008.cs.kuleuven.be/keynotes.php
  2. 2.
    Spector, A.: Distributed computing at multidimensional scale (2008), http://middleware2008.cs.kuleuven.be/AZSMiddleware08Keynote-Shared.pdf
  3. 3.
    Bouchenak, S., Palma, N.D., Hagimont, D., Taton, C.: Autonomic management of clustered applications. In: IEEE International Conference on Cluster Computing, Barcelona, Spain (September 2006)Google Scholar
  4. 4.
    Jung, G., Joshi, K.R., Hiltunen, M.A., Schlichting, R.D., Pu, C.: Generating adaptation policies for multi-tier applications in consolidated server environments. In: International Conference on Autonomic Computing, pp. 23–32 (2008)Google Scholar
  5. 5.
    Gunther, N.J.: Evaluating Scalability Parameters. In: Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services, ch. 5. Springer, New York (2006)Google Scholar
  6. 6.
    Transaction Processing Performance Council (TPC): TPC benchmark W (Web Commerce) Specification Version 1.7 (2001)Google Scholar
  7. 7.
    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the Spring Joint Computer Conference, AFIPS 1967 (Spring), April 18-20, pp. 483–485. ACM, New York (1967)CrossRefGoogle Scholar
  8. 8.
    Gunther, N.J.: Scalability - A Quantitative Approach. In: Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services, USA, ch. 4. Springer, New York (2006)Google Scholar
  9. 9.
    Gunther, N.J.: Software Scalability. In: Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services,  ch. 6. Springer, New York (2006)Google Scholar
  10. 10.
    Jain, R.: Operational Laws. In: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling, ch. 33. John Wiley & Sons, Inc., New York (1991)Google Scholar
  11. 11.
    Jain, R.: Analysis of a Single Queue. In: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling, ch. 31. John Wiley & Sons, Inc., New York (1991)Google Scholar
  12. 12.
    Jain, R.: Introduction to Queueing Theory. In: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling, ch. 30. John Wiley & Sons, Inc., New York (1991)Google Scholar
  13. 13.
    A simple network management protocol (SNMP). RFC 1157 (1990)Google Scholar
  14. 14.
    Java management extensions (JMX) (2004), http://java.sun.com/developer/technicalArticles/J2SE/jmx.html
  15. 15.
    Prasad, V., Cohen, W., Hunt, M., Keniston, J., Chen, B.: Architecture of systemtap: a linux trace/probe tool (2005), http://sourceware.org/systemtap/archpaper.pdf
  16. 16.
    Red Hat, IBM, Intel, Hitachi, Oracle and others: Systemtap (2010), http://sourceware.org/systemtap/
  17. 17.
    Wieërs, D.: Dstat (2009), http://dag.wieers.com/home-made/dstat/

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nuno A. Carvalho
    • 1
  • José Pereira
    • 1
  1. 1.Computer Science and Technology CenterUniversidade do MinhoBragaPortugal

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