Advertisement

The Journal of Supercomputing

, Volume 73, Issue 12, pp 5221–5238 | Cite as

Optimizing web server RAM performance using birth–death process queuing system: scalable memory issue

  • Zolfaghar Salmanian
  • Habib Izadkhah
  • Ayaz Isazadeh
Article
  • 248 Downloads

Abstract

Planning a powerful server imposes an enormous cost for providing ideal performance. Given that a server responding for web requests is more likely to consume RAM memory than other resources, it is desirable to provide an appropriate RAM capacity for optimal performance of server in congested situations. This can be done through RAM usage modeling and its performance evaluation. In the literature, modeling of RAM usage is not provided with mathematical modeling. We propose an approach to model RAM usage of such a server, based on birth–death process in this article. The model can be used to figure out an operation research problem of finding minimum RAM capacity covering intended constraints elicited from birth–death queuing system. We show how optimal RAM capacity can be obtained using our approach with an illustrative example.

Keywords

RAM provisioning quantification Server RAM performance evaluation RAM capacity management Continuous-time Markov chain Birth–death process Integer programming 

References

  1. 1.
    Arora RK (2015) Optimization: algorithms and applications. Taylor & Francis Group, Boca Raton. doi: 10.1201/b18469-8 CrossRefzbMATHGoogle Scholar
  2. 2.
    Bajo J, De la Prieta F, Corchado JM, Rodríguez S (2016) A low-level resource allocation in an agent-based cloud computing platform. Appl Soft Comput 48:716–728. doi: 10.1016/j.asoc.2016.05.056 CrossRefGoogle Scholar
  3. 3.
    Begain K, Bolch G, Herold H (2012) Practical performance modeling: application of the MOSEL language. The Springer international series in engineering and computer science. Springer, New YorkGoogle Scholar
  4. 4.
    Bolch G, Greiner S, de Meer H, Trivedi KS (2006) Queueing networks and Markov chains, 2nd edn. Wiley, Hoboken, NJCrossRefzbMATHGoogle Scholar
  5. 5.
    Booch G, Rumbaugh J, Jacobson I (2005) Unified modeling language user guide. The Addison-Wesley object technology series. Addison-Wesley Professional, BostonGoogle Scholar
  6. 6.
    Castro PH, Barreto VL, Corrêa SL, Granville LZ, Cardoso KV (2016) A joint CPU-RAM energy efficient and SLA-compliant approach for cloud data centers. Comput Netw 94:1–13. doi: 10.1016/j.comnet.2015.11.026 CrossRefGoogle Scholar
  7. 7.
    Farokhi S, Jamshidi P, Bayuh Lakew E, Brandic I, Elmroth E (2016) A hybrid cloud controller for vertical memory elasticity: a control-theoretic approach. Future Gener Comput Syst 65:57–72. doi: 10.1016/j.future.2016.05.028 CrossRefGoogle Scholar
  8. 8.
    Gokhale SS, Trivedi KS (2006) Analytical models for architecture-based software reliability prediction: a unification framework. IEEE Trans Reliab 55(4):578–590. doi: 10.1109/TR.2006.884587 CrossRefGoogle Scholar
  9. 9.
    Gross D, Shortle J, Thompson F, Harris C (2008) Fundamentals of queueing theory, 4th edn. Wiley, New York, NY. doi: 10.1017/CBO9781107415324.004 CrossRefzbMATHGoogle Scholar
  10. 10.
    Hagihara S, Fushihara Y, Shimakawa M, Tomoishi M, Yonezaki N (2017) Web server access trend analysis based on the Poisson distribution. In: Proceedings of the 6th International Conference on Software and Computer Applications, ICSCA ’17, pp 256–261. ACM, New York, NY. doi: 10.1145/3056662.3056701
  11. 11.
    Halfin S, Whitt W (1981) Heavy-traffic limits for queues with many exponential servers. Oper Res 29(3):567–588. doi: 10.1287/opre.29.3.567 CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Happe J, Becker S, Rathfelder C, Friedrich H, Reussner RH (2010) Parametric performance completions for model-driven performance prediction. Perform Eval 67(8):694–716CrossRefGoogle Scholar
  13. 13.
    Harchol-Balter M (2013) Performance modeling and design of computer systems: queueing theory in action, 1st edn. Cambridge University Press, New York, NYzbMATHGoogle Scholar
  14. 14.
    Haverkort BR (2001) Markovian models for performance and dependability evaluation. Springer, Berlin. doi: 10.1007/3-540-44667-2_2 CrossRefzbMATHGoogle Scholar
  15. 15.
    Hu Y, Nanda A, Yang Q (1999) Measurement, analysis and performance improvement of the Apache web server. In: Performance, Computing and... (November 1996), pp 1–18Google Scholar
  16. 16.
    Kant K, Srinivasan MM (1992) Introduction to computer system performance evaluation. McGraw-Hill computer science series. McGraw-Hill, New York CityGoogle Scholar
  17. 17.
    Liebowitz M, Kusek C, Spies R (2014) VMware VSphere performance: designing CPU, memory, storage, and networking for performance-intensive workloads. Wiley, Indianapolis, INGoogle Scholar
  18. 18.
    Lingo Systems (2016) LINDO software for integer programming, linear programming, nonlinear programming, stochastic programming, global optimization. http://www.lindo.com
  19. 19.
    Little JDC (2011) OR FORUM—Little’s law as viewed on its 50th anniversary. Oper Res 59(3):536–549. doi: 10.1287/opre.1110.0940 CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Little JDC, Graves SC (2008) Little’s law. In: Chhajed D, Lowe TJ (eds) Building intuition: insights from basic operations management models and principles, 1st edn. Springer, New York, pp 81–100. doi: 10.1007/978-0-387-73699-0 CrossRefGoogle Scholar
  21. 21.
    Liu HH (2009) Software performance and scalability: a quantitative approach. Wiley, Hoboken. doi: 10.1002/9780470465394 CrossRefzbMATHGoogle Scholar
  22. 22.
    Mo J (2010) Performance modeling of communication networks with Markov chains, vol 3. Morgan & Claypool, San Rafael. doi: 10.2200/S00269ED1V01Y201004CNT005 zbMATHGoogle Scholar
  23. 23.
    Sahner RA, Trivedi K, Puliafito A (2012) Performance and reliability analysis of computer systems: an example-based approach using the SHARPE software package. Springer, BerlinzbMATHGoogle Scholar
  24. 24.
    Sharma VS, Trivedi KS (2007) Quantifying software performance, reliability and security: an architecture-based approach. J Syst Softw 80(4):493–509CrossRefGoogle Scholar
  25. 25.
    Smith CU, Williams LG (2003) Software performance engineering. Springer, Boston, MA. doi: 10.1007/0-306-48738-1_16 Google Scholar
  26. 26.
    Thompson J, Gross D, Shortle J, Harris CH (2008) Queueing theory software plus toolbox 3.0. ftp://ftp.wiley.com/sci_tech_med/queueing_theory/qtsplus-xcel.exe
  27. 27.
    Vakilinia S, Ali MM, Qiu D (2015) Modeling of the resource allocation in cloud computing centers. Comput Netw 91:453–470. doi: 10.1016/j.comnet.2015.08.030 CrossRefGoogle Scholar
  28. 28.
    Vidgen R, Avison D, Wood B, Wood-Harper T (2002) Developing web information systems: from strategy to implementation. Butterworth-Heinemann information systems. Elsevier, AmsterdamGoogle Scholar
  29. 29.
    Whitt W (2007) What you should know about queueing models to set staffing requirements in service systems. Nav Res Logist 54(5):476–484. doi: 10.1002/nav.20243 CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of Mathematical SciencesUniversity of TabrizTabrizIran

Personalised recommendations