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Self-Aware Computing Systems pp 485–505Cite as

Run-Time Models for Online Performance and Resource Management in Data Centers

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Abstract

In this chapter, we introduce run-time models that a system may use for self-aware performance and resource management during operation. We focus on models that have been successfully used at run-time by a system itself or a system controller to reason about resource allocations and performance management in an online setting. This chapter provides an overview of existing classes of run-time models, including statistical regression models, queueing networks, control-theoretical models, and descriptive models. This chapter contributes to the state of the art, by creating a classification scheme, which we use to compare the different run-time model types. The aim of the scheme is to deepen the knowledge about the purpose, assumptions, and structure of each model class. We describe in detail two modeling case studies chosen because they are considered to be representative for a specific class of models. The description shows how these models can be used in a self-aware system for performance and resource management.

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References

  1. R. Alura, C. Courcoubetisb, N. Halbwachsc, T.A. Henzingerd, P.-H. Hod, X. Nicollinc, A. Oliveroc, J. Sifakis, and S. Yovinec. The algorithmic analysis of hybrid systems. Theoretical Computer Science, 138(6):3–34, Feb 1995. doi:10.1016/0304-3975(94)00202-T.

  2. Falko Bause. Queueing petri nets-a formalism for the combined qualitative and quantitative analysis of systems. In Petri Nets and Performance Models, 1993. Proceedings., 5th International Workshop on, pages 14–23. IEEE, 1993.

    Google Scholar 

  3. Steffen Becker, Heiko Koziolek, and Ralf Reussner. The Palladio component model for model-driven performance prediction. Journal of Systems and Software, 82:3–22, 2009.

    Article  Google Scholar 

  4. Mohamed N. Bennani and D. Menascé. Resource allocation for autonomic data centers using analytic performance models. In ICAC ’05: Proceedings of the Second International Conference on Automatic Computing, pages 229–240, Washington, DC, USA, 2005.

    Google Scholar 

  5. Gunter Bolch, Stefan Greiner, Hermann de Meer, and Kishor S Trivedi. Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. John Wiley & Sons, 2006.

    Google Scholar 

  6. Maury Bramson. A stable queueing network with unstable fluid model. The Annals of Applied Probability, 9(3):818–853, 1999.

    Article  MathSciNet  MATH  Google Scholar 

  7. M.S. Branicky. Stability of hybrid systems: state of the art. In Proceedings of the 36th Conference on Decision and Control, pages 120–125, San Diego, California USA, December 1997.

    Google Scholar 

  8. Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. Classification and regression trees. CRC press, 1984.

    Google Scholar 

  9. Fabian Brosig. Architecture-Level Software Performance Models for Online Performance Prediction. PhD thesis, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, 2014.

    Google Scholar 

  10. Fabian Brosig, Nikolaus Huber, and Samuel Kounev. Automated Extraction of Architecture-Level Performance Models of Distributed Component-Based Systems. In 26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011), 2011.

    Google Scholar 

  11. Yiyu Chen, Amitayu Das, Wubi Qin, Anand Sivasubramaniam, Qian Wang, and Natarajan Gautam. Managing server energy and operational costs in hosting centers. In Proceedings of the 2005 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’05, pages 303–314, New York, NY, USA, 2005. ACM.

    Google Scholar 

  12. Marc Courtois and Murray Woodside. Using regression splines for software performance analysis. In Proceedings of the 2Nd International Workshop on Software and Performance, WOSP ’00, pages 105–114, New York, NY, USA, 2000. ACM.

    Google Scholar 

  13. Roy T. Fielding and Richard N. Taylor. Principled design of the modern web architecture. ACM Trans. Internet Technol., 2(2):115–150, May 2002.

    Google Scholar 

  14. Antonio Filieri, Henry Hoffmann, and Martina Maggio. Automated design of self-adaptive software with control-theoretical formal guarantees. In Proc. of the 36th Intl. Conference on Software Engineering, pages 299–310, 2014.

    Google Scholar 

  15. Antonio Filieri, Henry Hoffmann, and Martina Maggio. Automated multi-objective control for self-adaptive software design. In Proceedings of the 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015. ACM, 2015.

    Google Scholar 

  16. Antonio Filieri, Martina Maggio, Konstantinos Angelopoulos, Nicolas D’Ippolito, and Ilias et al. Gerostathopoulos. Software engineering meets control theory. In Proc. of the 10th Intl. Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2015.

    Google Scholar 

  17. Jerome H Friedman. Multivariate adaptive regression splines. The annals of statistics, pages 1–67, 1991.

    Google Scholar 

  18. Sven Hedlund. Computational Methods for Optimal Control of Hybrid Systems. PhD thesis, Department of Automatic Control, Lund University, Sweden, May 2003.

    Google Scholar 

  19. T.A. Henzinger. The Theory of Hybrid Automata. In Proceedings of the Eleventh Annual IEEE Symposium on Logic in Computer Science (LICS), pages 278–292, 1996.

    Google Scholar 

  20. B. L. Ho and R. E. Kalman. Effective construction of linear state-variable models from input/output functions. Regelungstechnik, 14:545–548, 1966.

    MATH  Google Scholar 

  21. Tauseef A. Israr, Danny H. Lau, Greg Franks, and Murray Woodside. Automatic generation of layered queuing software performance models from commonly available traces. In Proc. of the 5th Intl. Workshop on Software and Performance, pages 147–158, 2005.

    Google Scholar 

  22. Rolf Johansson. System Modeling and Identification. Prentice Hall, Englewood Cliffs, New Jersey, January 1993.

    Google Scholar 

  23. Gueyoung Jung, M.A. Hiltunen, K.R. Joshi, R.D. Schlichting, and C. Pu. Mistral: Dynamically managing power, performance, and adaptation cost in cloud infrastructures. In Distributed Computing Systems (ICDCS), 2010 IEEE 30th Intl. Conf. on, pages 62 –73, 2010.

    Google Scholar 

  24. R. Kalman and R. Bucy. New results in linear filtering and prediction theory. Trans ASME, J. Basic Eng., ser. D, 83:95–107, 1961.

    Google Scholar 

  25. H.K. Khalil. Nonlinear Systems. Pearson Education. Prentice Hall, 2002.

    MATH  Google Scholar 

  26. Cristian Klein, Martina Maggio, Karl-Erik Årzén, and Francisco Hernández-Rodriguez. Brownout: Building more robust cloud applications. In Proceedings of the 36th International Conference on Software Engineering, pages 700–711, 2014.

    Google Scholar 

  27. Samuel Kounev, Fabian Brosig, and Nikolaus Huber. The Descartes Modeling Language. Technical report, Department of Computer Science, University of Wuerzburg, October 2014.

    Google Scholar 

  28. Samuel Kounev, Nikolaus Huber, Fabian Brosig, and Xiaoyun Zhu. Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures. IEEE Computer Magazine, 2016. Accepted for Publication.

    Google Scholar 

  29. M. Kuhn, S. Witson, C. Keefer, and N. Coulter. Cubist Models for Regression. http://cran.r-project.org/web/packages/Cubist/vignettes/cubist.pdf. Last accessed: Jul 2015.

  30. Jim Li, John Chinneck, Murray Woodside, Marin Litoiu, and Gabriel Iszlai. Performance model driven QoS guarantees and optimization in clouds. In Proc. of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, pages 15–22, 2009.

    Google Scholar 

  31. Awad M. and Daniel A. Menascé. Dynamic Derivation of Analytical Performance Models in Autonomic Computing Environments. In Proceedings of the 2014 Computer Measurement Group Performance and Capacity Conference (CMG), Nov 2014.

    Google Scholar 

  32. D. Menascé, Honglei Ruan, and Hassan Gomaa. Qos management in service-oriented architectures. Performance Evaluation, 64(7-8):646–663, August 2007.

    Google Scholar 

  33. V.S. Borkar M.S. Branicky and S.K. Mitter. A unified framework for hybrid control. In Proc. IEEE Conf. Decision and Control, pages 4228–4234, Lake Buena Vista, FL, Dec 1994.

    Google Scholar 

  34. Fiona Fui-Hoon Nah. A study on tolerable waiting time: how long are web users willing to wait? Behaviour and Information Technology, 23(3):153–163, 2004.

    Article  Google Scholar 

  35. Qais Noorshams, Dominik Bruhn, Samuel Kounev, and Ralf Reussner. Predictive Performance Modeling of Virtualized Storage Systems using Optimized Statistical Regression Techniques. In Proc. of the ACM/SPEC Intl. Conf. on Performance Engineering, pages 283–294, 2013.

    Google Scholar 

  36. Ramon Nou, Samuel Kounev, Ferran Julia, and Jordi Torres. Autonomic QoS control in enterprise Grid environments using online simulation. Journal of Systems and Software, 82(3):486–502, March 2009.

    Google Scholar 

  37. OMG. Meta Object Facility (MOF) Version 2.5, 2015.

    Google Scholar 

  38. G. Pacifici, M. Spreitzer, A. Tantawi, and A. Youssef. Performance Management of Cluster-Based Web Services. IEEE Journal on Selected Areas in Communications, 23(12):2333–2343, December 2005.

    Google Scholar 

  39. John R Quinlan et al. Learning with continuous classes. In 5th Australian joint conference on artificial intelligence, volume 92, pages 343–348. Singapore, 1992.

    Google Scholar 

  40. Abhishek B Sharma, Ranjita Bhagwan, Monojit Choudhury, Leana Golubchik, Ramesh Govindan, and Geoffrey M Voelker. Automatic request categorization in internet services. SIGMETRICS Perform. Eval. Rev., 36(2):16–25, Aug 2008.

    Google Scholar 

  41. Simon Spinner, Giuliano Casale, Fabian Brosig, and Samuel Kounev. Evaluating Approaches to Resource Demand Estimation. Performance Evaluation, 92:51 – 71, October 2015.

    Google Scholar 

  42. Simon Spinner, Samuel Kounev, and Philipp Meier. Stochastic Modeling and Analysis using QPME: Queueing Petri Net Modeling Environment v2.0. In Proc. of the 33rd Intl. Conf. on Application and Theory of Petri Nets and Concurrency, pages 388–397, 2012.

    Google Scholar 

  43. Bhuvan Urgaonkar, Giovanni Pacifici, Prashant Shenoy, Mike Spreitzer, and Asser Tantawi. Analytic modeling of multitier internet applications. ACM Trans. Web, 1(1), May 2007.

    Google Scholar 

  44. André van Hoorn. Online Capacity Management for Increased Resource Efficiency of Component-Based Software Systems. PhD thesis, University of Kiel, Germany, 2014.

    Google Scholar 

  45. P. van Overschee and B. de Moor. Subspace Identification for Linear Systems—Theory, Implementation, Applications. Kluwer Academic Publishers, Boston-London-Dordrect, 1996.

    Book  MATH  Google Scholar 

  46. Qi Zhang, Ludmila Cherkasova, and Evgenia Smirni. A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications. In Proceedings of the Fourth International Conference on Autonomic Computing, page 27ff, 2007.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Würzburg, Am Hubland, 97074, Würzburg, Germany

    Simon Spinner & Samuel Kounev

  2. Department of Computing, Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK

    Antonio Filieri

  3. Department of Automatic Control, Lund University, Ole Römers Väg 1, 223 63, Lund, Sweden

    Martina Maggio & Anders Robertsson

Authors
  1. Simon Spinner
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  2. Antonio Filieri
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  3. Samuel Kounev
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  4. Martina Maggio
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  5. Anders Robertsson
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Corresponding author

Correspondence to Simon Spinner .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Samuel Kounev

  2. Thomas J. Watson Research Center, Hawthorne, New York, USA

    Jeffrey O. Kephart

  3. University of Würzburg, Würzburg, Germany

    Aleksandar Milenkoski

  4. VMWare Inc.Futurewei Technologies, Inc. , Santa Clara, California, USA

    Xiaoyun Zhu

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Spinner, S., Filieri, A., Kounev, S., Maggio, M., Robertsson, A. (2017). Run-Time Models for Online Performance and Resource Management in Data Centers. In: Kounev, S., Kephart, J., Milenkoski, A., Zhu, X. (eds) Self-Aware Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-47474-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-47474-8_16

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  • Online ISBN: 978-3-319-47474-8

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