Abstract
Design decisions made during early development stages of self-adaptive systems tend to have a significant impact upon system properties at run time (e.g., safety, QoS). However, understanding the implications of these decisions a priori is difficult due to the different types and degrees of uncertainty that affect such systems (e.g., simplifying assumptions, human-in-the-loop). To provide some assurances about self-adaptive system designs, evidence can be gathered from activities such as simulations and prototyping, but these demand a significant effort and do not provide a systematic way of dealing with uncertainty. In this chapter, we describe an approach based on model checking of stochastic multiplayer games (SMGs) that enables developers to approximate the behavioral envelope of a self-adaptive system by analyzing best- and worst-case scenarios of alternative designs for self-adaptation mechanisms. Compared to other sources of evidence, such as simulations or prototypes, our approach is purely declarative and hence has the potential of providing developers with a preliminary understanding of adaptation behavior with less effort, and without the need to have any specific adaptation algorithms or infrastructure in place. We illustrate our approach by showing how it can be used to mitigate different types of uncertainty in contexts such as self-protecting systems, proactive latency-aware adaptation, and human-in-the-loop adaptation.
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Notes
- 1.
See Appendix A.2 in [11] for details.
- 2.
The variants of \(\mathsf {F}^{\star }\phi \) used for reward measurement in which the parameter \(\star \in \{ 0, \infty , c\}\) indicate that, when \(\phi \) is not reached, the reward is zero, infinite or equal to the cumulated reward along the whole path, respectively.
- 3.
Please refer to http://www.prismmodelchecker.org/doc/semantics.pdf for further details.
- 4.
We illustrate our approach to modeling the SMG using the syntax of the PRISM language [32], in which a process is encoded as a set of commands of the form:
$${[action]\,guard \rightarrow p_1:u_1 + \dots + p_n:u_n}$$Where guard is a predicate over variables in the model. Each update \(u_i\) describes a transition that the process can make (by executing action) if the guard is true. An update is specified by giving the new values of the variables, and has an assigned probability \(p_i \in [0,1]\). Multiple commands with overlapping guards (and commonly, including a single update of unspecified probability) introduce local nondeterminism.
- 5.
Our experiments assume that the predictive variant has access to a perfect prediction of the future evolution of the environment.
- 6.
We consider a simple version of Znn.com that adapts only by adjusting server pool size.
- 7.
TAU is the period of the self-adaptation control loop that includes the monitoring of the environment, and the adaptation decision.
- 8.
Note that for latency-aware adaptation \(U_{ega}=U_{rga}\).
- 9.
We herein consider a simplified version of the DCAS architecture. Further details about DCAS can be found in [7].
- 10.
In this context we refer to adaptation strategies as described in Stitch [15]. These correspond to decision trees in which branches are defined by means of condition-action-delay rules.
References
Alur, R., et al.: Alternating-time temporal logic. J. ACM 49(5), 672–713 (2002)
Baresi, L., Pasquale, L., Spoletini, P.: Fuzzy goals for requirements-driven adaptation. In: 2010 18th IEEE International Requirements Engineering Conference (RE), pp. 125–134, September 2010
Bianco, A., de Alfaro, L.: Model checking of probabilistic and nondeterministic systems. In: Thiagarajan, P.S. (ed.) FSTTCS 1995. LNCS, vol. 1026, pp. 499–513. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60692-0_70
Braberman, V.A., D’Ippolito, N., Piterman, N., Sykes, D., Uchitel, S.: Controller synthesis: from modelling to enactment. In: Notkin, D., Cheng, B.H.C., Pohl, K. (eds.) 35th International Conference on Software Engineering, ICSE 2013, San Francisco, CA, USA, 18–26 May 2013, pp. 1347–1350. IEEE/ACM (2013)
Calinescu, R., et al.: Dynamic QoS management and optimization in service-based systems. IEEE Trans. Software Eng. 37(3), 387–409 (2011)
Calinescu, R., Kwiatkowska, M.Z.: Using quantitative analysis to implement autonomic IT systems. In: ICSE (2009)
Cámara, J., Correia, P., de Lemos, R., Garlan, D., Gomes, P., Schmerl, B.R., Ventura, R.: Evolving an adaptive industrial software system to use architecture-based self-adaptation. In: Litoiu, M., Mylopoulos, J. (eds.) Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2013, San Francisco, CA, USA, 20–21 May 2013, pp. 13–22. IEEE/ACM (2013)
Cámara, J., Moreno, G.A., Garlan, D.: Stochastic game analysis and latency awareness for proactive self-adaptation. In: Engels, G., Bencomo, N. (eds.) Proceedings of 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2014, Hyderabad, India, 2–3 June 2014, pp. 155–164. ACM (2014)
Cámara, J., Moreno, G.A., Garlan, D.: Reasoning about human participation in self-adaptive systems. In: Schmerl, B., Inverardi, P. (eds.) Proceedings of 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2015, Florence, Italy, 18–19 May 2015. ACM (2015)
Chen, T., Forejt, V., Kwiatkowska, M., Parker, D., Simaitis, A.: PRISM-games: a model checker for stochastic multi-player games. In: Piterman, N., Smolka, S.A. (eds.) TACAS 2013. LNCS, vol. 7795, pp. 185–191. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36742-7_13
Chen, T., Forejt, V., Kwiatkowska, M.Z., Parker, D., Simaitis, A.: Automatic verification of competitive stochastic systems. Formal Methods Syst. Des. 43(1), 61–92 (2013)
Chen, T., Lu, J.: Probabilistic alternating-time temporal logic and model checking algorithm. In: FSKD, vol. 2 (2007)
Cheng, B.H.C., et al.: Software engineering for self-adaptive systems: a research roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 1–26. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_1
Cheng, S., Garlan, D., Schmerl, B.R.: Evaluating the effectiveness of the rainbow self-adaptive system. In: 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2009, Vancouver, BC, Canada, 18–19 May 2009, pp. 132–141. IEEE (2009)
Cheng, S.-W., Garlan, D.: Stitch: a language for architecture-based self-adaptation. J. Syst. Softw. 85(12), 2860–2875 (2012)
Chiulli, R.: Quantitative analysis: an introduction. In: Automation and Production Systems. Taylor & Francis (1999)
Deshpande, T., Katsaros, P., Smolka, S., Stoller, S.: Stochastic game-based analysis of the DNS bandwidth amplification attack using probabilistic model checking. In: 2014 Tenth European Dependable Computing Conference (EDCC), pp. 226–237, May 2014
Elkhodary, A., Esfahani, N., Malek, S.: FUSION: a framework for engineering self-tuning self-adaptive software systems. In: Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2010, pp. 7–16, New York, NY, USA. ACM (2010)
Emami-Taba, M., Amoui, M., Tahvildari, L.: Strategy-aware mitigation using Markov games for dynamic application-layer attacks. In: 2015 IEEE 16th International Symposium on High Assurance Systems Engineering (HASE), pp. 134–141, January 2015
Epifani, I., et al.: Model evolution by run-time parameter adaptation. In: ICSE. IEEE CS (2009)
Epifani, I., Ghezzi, C., Mirandola, R., Tamburrelli, G.: Model evolution by run-time parameter adaptation. In: Atlee, J.M., Inverardi, P. (eds.) Proceedings of 31st International Conference on Software Engineering, ICSE 2009, 16–24 May 2009, Vancouver, Canada, pp. 111–121. IEEE (2009)
Esfahani, N., Kouroshfar, E., Malek, S.: Taming uncertainty in self-adaptive software. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, ESEC/FSE 2011, pp. 234–244. ACM (2011)
Esfahani, N., Malek, S.: Uncertainty in self-adaptive software systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 214–238. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35813-5_9
Eskins, D., Sanders, W.H.: The multiple-asymmetric-utility system model: a framework for modeling cyber-human systems. In: Eighth International Conference on Quantitative Evaluation of Systems, QEST 2011, Aachen, Germany, 5–8 September 2011, pp. 233–242. IEEE Computer Society (2011)
Forejt, V., Kwiatkowska, M., Norman, G., Parker, D.: Automated verification techniques for probabilistic systems. In: Bernardo, M., Issarny, V. (eds.) SFM 2011. LNCS, vol. 6659, pp. 53–113. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21455-4_3
Garlan, D., Cheng, S., Huang, A., Schmerl, B.R., Steenkiste, P.: Rainbow: architecture-based self-adaptation with reusable infrastructure. IEEE Comput. 37(10), 46–54 (2004)
Goldman, R.P., Musliner, D.J., Krebsbach, K.D.: Managing online self-adaptation in real-time environments. In: Laddaga, R., Shrobe, H., Robertson, P. (eds.) IWSAS 2001. LNCS, vol. 2614, pp. 6–23. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36554-0_2
He, K., Zhang, M., He, J., Chen, Y.: Probabilistic model checking of pipe protocol. In: 2015 International Symposium on Theoretical Aspects of Software Engineering (TASE), pp. 135–138, September 2015
Huebscher, M.C., McCann, J.A.: A survey of autonomic computing - degrees, models, and applications. ACM Comput. Surv. 40(3) (2008)
Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36, 41–50 (2003)
Kolmogorov, A.N.: Foundations of the Theory of Probability. Chelsea, New York (1956)
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22110-1_47
Kwiatkowska, M., Norman, G., Parker, D.: Stochastic games for verification of probabilistic timed automata. In: Ouaknine, J., Vaandrager, F.W. (eds.) FORMATS 2009. LNCS, vol. 5813, pp. 212–227. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04368-0_17
Li, W., Sadigh, D., Sastry, S.S., Seshia, S.A.: Synthesis for human-in-the-loop control systems. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014. LNCS, vol. 8413, pp. 470–484. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54862-8_40
Myerson, R.B.: Game Theory: Analysis of Conflict. Harvard University Press, Cambridge (1991)
Okhravi, H., Rabe, M., Mayberry, T., Leonard, W., Hobson, T., Bigelow, D., Streilein, W.: Survey of cyber moving target techniques. Technical report 1166, Lincoln Laboratory, Massachusetts Institute of Technology (2013)
Schmerl, B., Cámara, J., Gennari, J., Garlan, D., Casanova, P., Moreno, G.A., Glazier, T.J., Barnes, J.M.: Architecture-based self-protection: composing and reasoning about denial-of-service mitigations. In: Proceedings of the 2014 Symposium and Bootcamp on the Science of Security, HotSoS 2014, New York, NY, USA, pp. 2:1–2:12. ACM (2014)
Schmerl, B., Camara, J., Gennari, J., Garlan, D., Casanova, P., Moreno, G.A., Glazier, T.J., Barnes, J.M.: Architecture-based self-protection: composing and reasoning about denial-of-service mitigations. In: Symposium and Bootcamp on the Science of Security (HotSoS), Raleigh, USA, 8–9 April 2014
Whittle, J., Sawyer, P., Bencomo, N., Cheng, B., Bruel, J.: Relax: incorporating uncertainty into the specification of self-adaptive systems. In: 17th IEEE International Requirements Engineering Conference, RE 2009, pp. 79–88, August 2009
Yuan, E., Esfahani, N., Malek, S.: A systematic survey of self-protecting software systems. ACM Trans. Auton. Adapt. Syst. (TAAS) 8(4), 17 (2014)
Yuan, E., Malek, S., Schmerl, B., Garlan, D., Gennari, J.: Architecture-based self-protecting software systems. In: Proceedings of the Ninth International ACM Sigsoft Conference on the Quality of Software Architectures (QoSA 2013), 17–21 June 2013
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Zadeh, L.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 100(Supplement 1), 9–34 (1999)
Zhang, X., Lung, C.: Improving software performance and reliability with an architecture-based self-adaptive framework. In: Ahamed, S.I., Bae, D., Cha, S.D., Chang, C.K., Subramanyan, R., Wong, E., Yang, H. (eds.) Proceedings of the 34th Annual IEEE International Computer Software and Applications Conference, COMPSAC 2010, Seoul, Korea, 19–23 July 2010, pp. 72–81. IEEE Computer Society (2010)
Acknowledgements
This work is supported in part by awards N000141310401 and N000141310171 from the Office of Naval Research, CNS-0834701 from the National Science Foundation, and by the National Security Agency. The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research or the U.S. government. This material is also based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. This material has been approved for public release and unlimited distribution. (DM-0002292).
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Cámara, J., Garlan, D., Moreno, G.A., Schmerl, B. (2017). Analyzing Self-Adaptation Via Model Checking of Stochastic Games. In: de Lemos, R., Garlan, D., Ghezzi, C., Giese, H. (eds) Software Engineering for Self-Adaptive Systems III. Assurances. Lecture Notes in Computer Science(), vol 9640. Springer, Cham. https://doi.org/10.1007/978-3-319-74183-3_6
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