Advertisement

Supporting Performance Awareness in Autonomous Ensembles

  • Lubomír Bulej
  • Tomáš Bureš
  • Ilias Gerostathopoulos
  • Vojtěch Horký
  • Jaroslav Keznikl
  • Lukáš Marek
  • Max Tschaikowski
  • Mirco Tribastone
  • Petr Tůma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8998)

Abstract

The ASCENS project works with systems of self-aware, self-adaptive and self-expressive ensembles. Performance awareness represents a concern that cuts across multiple aspects of such systems, from the techniques to acquire performance information by monitoring, to the methods of incorporating such information into the design making and decision making processes. This chapter provides an overview of five project contributions – performance monitoring based on the DiSL instrumentation framework, measurement evaluation using the SPL formalism, performance modeling with fluid semantics, adaptation with DEECo and design with IRM-SA – all in the context of the cloud case study.

Keywords

performance monitoring modeling adaptive systems autonomic systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aoki, M.: Control of large-scale dynamic systems by aggregation. IEEE Trans. Autom. Control 13(3) (1968)Google Scholar
  2. 2.
    ASM (2014), http://asm.ow2.org/
  3. 3.
    Barendregt, H.: The Lambda Calculus: Its Syntax and Semantics. Mathematical Programming Study. North-Holland Publishing Company, Amsterdam (1984)zbMATHGoogle Scholar
  4. 4.
    Bruni, R., Corradini, A., Gadducci, F., Hölzl, M., Lafuente, A.L., Vandin, A., Wirsing, M.: Reconciling White-Box and Black-Box Perspectives on Behavioral Self-adaptation. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 163–184. Springer, Heidelberg (2015)Google Scholar
  5. 5.
    Buchholz, P.: Exact and ordinary lumpability in finite Markov chains. Journal of Applied Probability 31(1) (1994)Google Scholar
  6. 6.
    Bulej, L., Bureš, T., Horký, V., Keznikl, J., Tůma, P.: Performance awareness in component systems: Vision paper. In: Proc. COMPSAC 2012 CORCS (2012)Google Scholar
  7. 7.
    Bulej, L., Bureš, T., Horký, V., Keznikl, J.: Adaptive deployment in ad-hoc systems using emergent component ensembles: Vision paper. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE ’13), ACM Press, New York (2013)Google Scholar
  8. 8.
    Bulej, L., Bureš, T., Horký, V., Kotrč, J., Marek, L., Trojánek, T., Tůma, P.: SPL: Unit testing performance. Tech. Rep. D3S-TR-2014-04, Dep. of Distributed and Dependable Systems, Charles University in Prague (2014)Google Scholar
  9. 9.
    Bulej, L., Bureš, T., Keznikl, J., Koubková, A., Podzimek, A., Tůma, P.: Capturing performance assumptions using stochastic performance logic. In: Proc. ICPE 2012, ACM Press, New York (2012)Google Scholar
  10. 10.
    Bureš, T., Horký, V., Kit, M., Marek, L., Tůma, P.: Towards performance-aware engineering of autonomic component ensembles. In: Margaria, T., Steffen, B. (eds.) ISoLA 2014, Part I. LNCS, vol. 8802, pp. 131–146. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Bures, T., Gerostathopoulos, I., Hnetynka, P., Keznikl, J., Kit, M., Plasil, F.: DEECo – an ensemble-based component system. In: Proc. of the International ACM SIGSOFT Symposium on Component Based Software Engineering (CBSE ’13), Vancouver, Canada, ACM, New York (2013)Google Scholar
  12. 12.
    Bureš, T., Gerostathopoulos, I., Hnetynka, P., Keznikl, J., Kit, M., Plasil, F.: The Invariant Refinement Method. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 405–428. Springer, Heidelberg (2015)Google Scholar
  13. 13.
    Cantrill, B.M., Shapiro, M.W., Leventhal, A.H.: Dynamic instrumentation of production systems. In: Proceedings of the USENIX Annual Technical Conference (ATC’04), Berkeley, CA, USA (2004)Google Scholar
  14. 14.
    Cardelli, L.: On process rate semantics. Theor. Comput. Sci. 391 (2008)Google Scholar
  15. 15.
    Chiba, S.: Load-time structural reflection in Java. In: Bertino, E. (ed.) ECOOP 2000. LNCS, vol. 1850, p. 313. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  16. 16.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: A framework for the modelling and analysis of biological systems. Theor. Comput. Sci. 410(33–34) (2009)Google Scholar
  17. 17.
    Clark, M.: JUnitPerf (2014), http://www.clarkware.com/software/JUnitPerf
  18. 18.
    Hayden, R.A., Bradley, J.T.: A fluid analysis framework for a Markovian process algebra. Theor. Comput. Sci. 411(22-24) (2010)Google Scholar
  19. 19.
    Herbst, N.R., Huber, N., Kounev, S., Amrehn, E.: Self-adaptive workload classification and forecasting for proactive resource provisioning. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE ’13), ACM Press, New York (2013)Google Scholar
  20. 20.
    Hermanns, H., Rettelbach, M.: Syntax, semantics, equivalences, and axioms for MTIPP. In: Proceedings of Process Algebra and Probabilistic Methods, Erlangen (1994)Google Scholar
  21. 21.
    Hillston, J.: Fluid flow approximation of PEPA models. In: Proceedings of Quantitative Evaluation of Systems, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  22. 22.
    Hillston, J.: A compositional approach to performance modelling. Cambridge University Press, New York (1996)CrossRefGoogle Scholar
  23. 23.
    Hölzl, M., Gabor, T.: Reasoning and Learning for Awareness and Adaptation. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 249–290. Springer, Heidelberg (2015)Google Scholar
  24. 24.
    Hölzl, M., Koch, N., Puviani, M., Wirsing, M., Zambonelli, F.: The Ensemble Development Life Cycle and Best Practices for Collective Autonomic Systems. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 325–354. Springer, Heidelberg (2015)Google Scholar
  25. 25.
    Horký, V., Haas, F., Kotrč, J., Lacina, M., Tůma, P.: Performance regression unit testing: a case study. In: Balsamo, M.S., Knottenbelt, W.J., Marin, A. (eds.) EPEW 2013. LNCS, vol. 8168, pp. 149–163. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  26. 26.
    Horký, V., Libič, P., Marek, L., Steinhauser, A., Tůma, P.: Utilizing performance unit tests to increase performance awareness. In: Proc. ICPE 2015, ACM Press, New York (2015)Google Scholar
  27. 27.
    Iacobelli, G., Tribastone, M.: Lumpability of fluid models with heterogeneous agent types. In: DSN (2013)Google Scholar
  28. 28.
    Iwase, Y., Levin, S.A., Andreasen, V.: Aggregation in model ecosystems I: perfect aggregation. Ecological Modelling 37 (1987)Google Scholar
  29. 29.
    JDOM Library (2013), http://www.jdom.org
  30. 30.
    Kalibera, T., Bulej, L., Tůma, P.: Benchmark precision and random initial state. In: Proc. SPECTS 2005, pp. 853–862. SCS (2005)Google Scholar
  31. 31.
    Kalibera, T., Bulej, L., Tuma, P.: Automated detection of performance regressions: the Mono experience. In: 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Sep. 2005, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  32. 32.
    Keznikl, J., Bures, T., Plasil, F., Gerostathopoulos, I., Hnetynka, P., Hoch, N.: Design of ensemble-based component systems by invariant refinement. In: Proc. of the 16th International ACM SIGSOFT Symposium on Component Based Software Engineering (CBSE ’13), Vancouver, Canada, ACM, New York (2013)Google Scholar
  33. 33.
    Kiczales, G., Hilsdale, E., Hugunin, J., Kersten, M., Palm, J., Griswold, W.G.: An overview of aspectJ. In: Knudsen, J.L. (ed.) ECOOP 2001. LNCS, vol. 2072, p. 327. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  34. 34.
    Kwiatkowski, M., Stark, I.: The continuous π-calculus: A process algebra for biochemical modelling. In: Heiner, M., Uhrmacher, A.M. (eds.) CMSB 2008. LNCS (LNBI), vol. 5307, pp. 103–122. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  35. 35.
    Marek, L., Zheng, Y., Ansaloni, D., Bulej, L., Sarimbekov, A., Binder, W., Tůma, P.: Introduction to dynamic program analysis with DiSL. Science of Computer Programming (2014)Google Scholar
  36. 36.
    Marek, L., Zhen, Y., Binder, W.: DiSL (2012), http://d3s.mff.cuni.cz/software/disl
  37. 37.
    Marek, L., Zheng, Y., Ansaloni, D., Binder, W., Qi, Z., Tuma, P.: DiSL: An extensible language for efficient and comprehensive dynamic program analysis. In: Proc. 7th Workshop on Domain-Specific Aspect Languages (DSAL ’12), ACM Press, New York (2012)Google Scholar
  38. 38.
    Mayer, P., Velasco, J., Klarl, A., Hennicker, R., Puviani, M., Tiezzi, F., Pugliese, R., Keznikl, J., Bureš, T.: The Autonomic Cloud. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 495–512. Springer, Heidelberg (2015)Google Scholar
  39. 39.
    Milner, R.: Communication and Concurrency. Prentice-Hall, Inc., Upper Saddle River (1989)zbMATHGoogle Scholar
  40. 40.
    Mytkowicz, T., Diwan, A., Hauswirth, M., Sweeney, P.F.: Producing wrong data without doing anything obviously wrong. In: Proceedings of ASPLOS 2009, ACM Press, New York (2009)Google Scholar
  41. 41.
    De Nicola, R., Latella, D., Lafuente, A.L., Loreti, M., Margheri, A., Massink, M., Morichetta, A., Pugliese, R., Tiezzi, F., Vandin, A.: The SCEL Language: Design, Implementation, Verification. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 3–71. Springer, Heidelberg (2015)Google Scholar
  42. 42.
    Okino, M.S., Mavrovouniotis, M.L.: Simplification of mathematical models of chemical reaction systems. Chemical Reviews 2(98) (1998)Google Scholar
  43. 43.
  44. 44.
  45. 45.
    Perl, S.E., Weihl, W.E.: Performance assertion checking. SIGOPS Oper. Syst. Rev. 27 (1993)Google Scholar
  46. 46.
    Reynolds, P., Killian, C., Wiener, J.L., Mogul, J.C., Shah, M.A., Vahdat, A.: Pip: Detecting the Unexpected in Distributed Systems. In: NSDI’06. USENIX (2006)Google Scholar
  47. 47.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2011)zbMATHGoogle Scholar
  48. 48.
  49. 49.
  50. 50.
    Tahchiev, P., Leme, F., Massol, V., Gregory, G.: JUnit in Action, 2nd edn. (2010)Google Scholar
  51. 51.
    Tribastone, M., Gilmore, S., Hillston, J.: Scalable differential analysis of process algebra models. IEEE Transactions on Software Engineering 38(1) (2012)Google Scholar
  52. 52.
    Tschaikowski, M., Tribastone, M.: Exact fluid lumpability for Markovian process algebra. In: Koutny, M., Ulidowski, I. (eds.) CONCUR 2012. LNCS, vol. 7454, pp. 380–394. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  53. 53.
    Tschaikowski, M., Tribastone, M.: Tackling continuous state-space explosion in a Markovian process algebra. Theoretical Computer Science 517 (2014)Google Scholar
  54. 54.
    Tschaikowski, M., Tribastone, M.: A unified framework for differential aggregations in Markovian process algebra. Journal of Logical and Algebraic Methods in Programming (2014)Google Scholar
  55. 55.
    Vetter, J.S., Worley, P.H.: Asserting Performance Expectations. In: Proc. 2002 ACM/IEEE Conf. on Supercomputing (Supercomputing ’02), IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  56. 56.
    Welch, B.L.: The generalization of student’s problem when several different population variances are involved. Biometrika 34(1/2) (1947)Google Scholar
  57. 57.
    Wirsing, M., Hölzl, M.M., Tribastone, M., Zambonelli, F.: ASCENS: Engineering Autonomic Service-Component Ensembles. In: Beckert, B., Damiani, F., de Boer, F.S., Bonsangue, M.M. (eds.) FMCO 2011. LNCS, vol. 7542, pp. 1–24. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lubomír Bulej
    • 1
  • Tomáš Bureš
    • 1
  • Ilias Gerostathopoulos
    • 1
  • Vojtěch Horký
    • 1
  • Jaroslav Keznikl
    • 1
  • Lukáš Marek
    • 1
  • Max Tschaikowski
    • 2
  • Mirco Tribastone
    • 2
  • Petr Tůma
    • 1
  1. 1.Department of Distributed and Dependable Systems, Faculty of Mathematics and PhysicsCharles UniversityCzech Republic
  2. 2.Electronics and Computer ScienceUniversity of SouthamptonUnited Kingdom

Personalised recommendations