Abstract
An increasingly important concern for modern systems design is how best to incorporate self-adaptation into systems so as to improve their ability to dynamically respond to faults, resource variation, and changing user needs. One promising approach is to use architectural models as a basis for monitoring, problem detection, and repair selection. While this approach has been shown to yield positive results, current systems use a reactive approach: they respond to problems only when they occur. In this paper we argue that self-adaptation can be improved by adopting an anticipatory approach in which predictions are used to inform adaptation strategies. We show how such an approach can be incorporated into an architecture-based adaptation framework and demonstrate the benefits of the approach.
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References
Batista, T.V., Joolia, A., Coulson, G.: Managing dynamic reconfiguration in component-based systems. In: Morrison, R., Oquendo, F. (eds.) EWSA 2005. LNCS, vol. 3527, pp. 1–17. Springer, Heidelberg (2005)
Bent, R., van Hentenryck, P.: Regrets only! Online stochastic optimization under time constraints. In: Proc. 19th AAAI (2004)
Cheng, S.-W.: Rainbow: Cost-Effective Software Architecture-Based Self-Adaptation, Ph.D. Thesis, TR CMU-ISR-08-113, Carnegie Mellon University School of Computer Science (May 2008)
Cheng, S.-W., Garlan, D., Schmerl, B.: Making Self-Adaptation and Engineering Reality. In: Babaoğlu, Ö., Jelasity, M., Montresor, A., Fetzer, C., Leonardi, S., van Moorsel, A., van Steen, M. (eds.) SELF-STAR 2004. LNCS, vol. 3460, pp. 158–173. Springer, Heidelberg (2005)
Clements, P., et al.: Documenting Software Architecture: Views and Beyond. Pearson Education, London (2003)
Dashofy, E.M., van der Hoek, A., Taylor, R.N.: Towards architecture-based self-healing systems. In: Garlan, et al. [10], pp. 21–26 (2002)
Dinda, P., O’Halloran, D.: Host Load Prediction Using Linear Models. Cluster Computing 3, 4 (2000)
Frye, C.: Self-healing systems. Appl. Dev. Trends, 29–34 (September 2003)
Galtier, V., et al.: Predicting resource demand in heterogeneous active networks. In: Proc. MILCOM (2001)
Garlan, D., Kramer, J., Wolf, A. (eds.): Proc. 1st ACM SIGSOFT Workshop on Self-Healing Systems (WOSS 2002), November 18–19. ACM Press, New York (2002)
Georgiadis, I., Magee, J., Kramer, J.: Self-organizing software architectures for distributed systems. In: Garlan, et al. [10], pp. 33–38 (2002)
Ghosh, D., Sharman, R., Rao, H.R., Upadhyaya, S.: Self-healing systems - survey and synthesis. Decision Support System 42(4), 2164–2185 (2007)
Gorlick, M.M., Razouk, R.R.: Using Weaves for software construction and analysis. In: Proc. 13th International Conf. of Software Engineering, pp. 23–34. IEEE Computer Society Press, Los Alamitos (1991)
Hentenryck, P., et al.: Online stochastic optimization under time constraints (2008), http://www.cs.brown.edu/people/pvh/aor5.pdf (last accessed April 2008)
Lu, Y., Abdelzaher, T., Lu, C., Sha, L., Liu, X.: Feedback Control with Queuing-Theoretic Prediction for Relative Delay Guarantees in Web Servers. In: Proc. IEEE Real-Time and Embedded Technology and Applications Symposium (2003)
Magee, J., Kramer, J.: Dynamic structure in software architectures. In: SIGSOFT 1996: Proc. of the 4th ACM SIGSOFT Symposium on Foundations of Software Engineering, pp. 3–14. ACM, New York (1996)
Morrison, R., Balasubramaniam, D., Oquendo, F., Warboys, B., Greenwood, R.M.: An active architecture approach to dynamic systems co-evolution. In: Oquendo, F. (ed.) ECSA 2007. LNCS, vol. 4758, pp. 2–10. Springer, Heidelberg (2007)
Mukhija, A., Glinz, M.: A framework for dynamically adaptive applications in a self-organized mobile network environment. In: ICDCSW 2004: Proceedings of the 24th International Conference on Distributed Computing Systems Workshops—W7: EC (ICDCSW 2004), pp. 368–374. IEEE Computer Society, Washington (2004)
Oreizy, P., et al.: An architecture-based approach to self-adaptive software. IEEE Intelligent Systems 14(3), 54–62 (1999)
Poladian, V., Garlan, D., Shaw, M., Schmerl, B., Sousa, J.P., Satyanarayanan, M.: Leveraging Resource Prediction for Anticipatory Dynamic Configuration. In: Proc. 1st IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007), July 2007, pp. 214–223 (2007)
Poladian, V.: Tailoring Configuration to User’s Tasks under Uncertainty, Ph.D. Thesis, TR CMU-CS-08-121, Carnegie Mellon University School of Computer Science (May 2008)
Russel, L., Morgan, S., Chron, E.: Clockwork: A new movement in autonomic systems. IBM Systems Journal 42, 1 (2003)
Solomon, B., Ionescu, D., Litoiu, M., Mihaescu, M.: A Real-Time Adaptive Control of Autonomic Computing Environments. In: Proc. 4th International Information and Telecommunication Technologies Symposium (U2TS 2006), December 2006, pp. 94–103 (2006)
Sousa, J.P.: Scaling Task Management in Space and Time: Reducing User Overhead in Ubiquitous-Computing Environments, Ph.D. Thesis, TR CMU-CS-05-123, Carnegie Mellon University School of Computer Science (2005)
Sztajnberg, A., Loques, O.: Describing and deploying self-adaptive applications. In: Proc. 1st Latin American Autonomic Computing Symposium, July 14–20 (2006)
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Cheng, SW., Poladian, V.V., Garlan, D., Schmerl, B. (2009). Improving Architecture-Based Self-Adaptation through Resource Prediction. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds) Software Engineering for Self-Adaptive Systems. Lecture Notes in Computer Science, vol 5525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02161-9_4
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DOI: https://doi.org/10.1007/978-3-642-02161-9_4
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