Using Evolution Graphs for Describing Topology-Aware Prediction Models in Large Clusters

  • Matei Popovici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7486)


We present and formally investigate a modelling method suitable for describing events and time-dependent properties and for performing possibly complex reasoning tasks regarding the evolution of dynamic domains. Our proposal consists of a distinguished data structure called evolution graph, and a logical language (\(\ensuremath{L_\ensuremath{\mathcal{H}}}\)) used for identifying temporal patterns in evolution graphs. First, we define and study the complexity of the model checking problem for our language. We then investigate the relation between our language and the well-known Computation Tree Logic (CTL), both in terms of complexity and expressive power. Finally, we apply our method for solving a well-known problem from High Performance Computing (HPC): the extraction of topology information from event logs produced by supercomputers.


temporal knowledge representation temporal logic high-performance computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Almási, G.S., Bellofatto, R., Brunheroto, J.R., Caşcaval, C., Castaños, J.G., Ceze, L., Crumley, P., Erway, C.C., Gagliano, J., Lieber, D., Martorell, X., Moreira, J.E., Sanomiya, A., Strauss, K.: An Overview of the Blue Gene/L System Software Organization. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 543–555. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Gainaru, A., Franck Cappello, W.K.: Taming of the shrew: Modeling the normal and faulty behavior of large-scale hpc systems. In: Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS), pp. 24–35 (to appear, 2012)Google Scholar
  3. 3.
    Artale, A., Franconi, E.: A survey of temporal extensions of description logics. Annals of Mathematics and Artificial Intelligence 30, 171–210 (2001)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)Google Scholar
  5. 5.
    Bordini, R.H., Wooldridge, M., Hübner, J.F.: Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology). John Wiley & Sons (2007)Google Scholar
  6. 6.
    Chandra, A.K., Merlin, P.M.: Optimal implementation of conjunctive queries in relational data bases. In: Proceedings of the Ninth Annual ACM Symposium on Theory of Computing, STOC 1977, pp. 77–90. ACM, New York (1977), CrossRefGoogle Scholar
  7. 7.
    Clarke, E.M., Emerson, E.A., Sistla, A.P.: Automatic verification of finite-state concurrent systems using temporal logic specifications. ACM Trans. Program. Lang. Syst. 8(2), 244–263 (1986)CrossRefMATHGoogle Scholar
  8. 8.
    De Nicola, R., Vaandrager, F.: Action versus state based logics for transition systems. In: Proceedings of the LITP Spring School on Theoretical Computer Science on Semantics of Systems of Concurrent Processes, pp. 407–419. Springer-Verlag New York, Inc., New York (1990), Google Scholar
  9. 9.
    Emerson, E.A.: Model checking and the mu-calculus. In: Descriptive Complexity and Finite Models, pp. 185–214 (1996)Google Scholar
  10. 10.
    Capello, F., Al Geist, B.G.S.K.B.K.M.S.: Toward exascale resilience. International Journal of High Performance Computing Applications 23 (2009)Google Scholar
  11. 11.
    Gainaru, A., Cappello, F., Trausan-Matu, S., Kramer, B.: Event Log Mining Tool for Large Scale HPC Systems. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 52–64. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Gallet, M., Yigitbasi, N., Javadi, B., Kondo, D., Iosup, A., Epema, D.: A Model for Space-Correlated Failures in Large-Scale Distributed Systems. In: D’Ambra, P., Guarracino, M., Talia, D. (eds.) Euro-Par 2010, Part I. LNCS, vol. 6271, pp. 88–100. Springer, Heidelberg (2010), CrossRefGoogle Scholar
  13. 13.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: an architecture for general intelligence. Artif. Intell. 33(1), 1–64 (1987), Google Scholar
  14. 14.
    Lan, Z., Zheng, Z., Li, Y.: Toward automated anomaly identification in large-scale systems. IEEE Trans. on Parallel and Distributed Systems 21(2), 174–187 (2010)CrossRefGoogle Scholar
  15. 15.
    Libkin, L.: Elements Of Finite Model Theory. Texts in Theoretical Computer Science. An Eatcs Series. Springer (2004)Google Scholar
  16. 16.
    Oldfield, R.A., Arunagiri, S., Teller, P.J., Seelam, S., Varela, M.R., Riesen, R., Roth, P.C.: Modeling the impact of checkpoints on next-generation systems. In: Proceedings of the 24th IEEE Conference on Mass Storage Systems and Technologies, MSST 2007, pp. 30–46. IEEE Computer Society, Washington, DC (2007)CrossRefGoogle Scholar
  17. 17.
    Pan, F.: An Ontology of Time: Representing Complex Temporal Phenomena for the Semantic Web and Natural Language. VDM Verlag, Saarbrucken (2009)Google Scholar
  18. 18.
    Park, Geist, A.: System log pre-processing to improve failure prediction. In: DSN 2009, pp. 572–577 (June 2009)Google Scholar
  19. 19.
    Pnueli, A.: The temporal logic of programs. In: Proceedings of the 18th Annual Symposium on Foundations of Computer Science, SFCS 1977, pp. 46–57. IEEE Computer Society, Washington, DC (1977)CrossRefGoogle Scholar
  20. 20.
    Popovici, M., Muraru, M., Agache, A., Giumale, C., Negreanu, L., Dobre, C.: A Modeling Method and Declarative Language for Temporal Reasoning Based on Fluid Qualities. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds.) ICCS-ConceptStruct 2011. LNCS, vol. 6828, pp. 215–228. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Riley, G.: NASA Clips: A Tool for Building Expert Systems (June 2006),
  22. 22.
    Roşu, G., Bensalem, S.: Allen Linear (Interval) Temporal Logic – Translation to LTL and Monitor Synthesis. In: Ball, T., Jones, R.B. (eds.) CAV 2006. LNCS, vol. 4144, pp. 263–277. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42(3), 10:1–10:42 (2010)Google Scholar
  24. 24.
    Schnoebelen, P.: The complexity of temporal logic model checking. In: Proceedings of Advances in Modal Logics AiML 2002. World Scientific (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matei Popovici
    • 1
  1. 1.POLITEHNICA University of BucharestBucharestRomania

Personalised recommendations