Abstract
By restructuring and reconfiguring itself at run-time, a collective adaptive system (CAS) is able to fulfill its requirements under uncertain, ever-changing environmental conditions. Indeed, this process of self-organization (SO) is of utmost importance for the ability of the CAS to perform. However, it is hard to design high-performing SO mechanisms, because the environmental conditions are partially unpredictable at design time. Thus, a crucial aid for the development of SO mechanisms is a tool set enabling performance evaluations at design time in order to select the best-fitting mechanism and parametrize it. We present a metric for measuring the performance of an SO mechanism as well as a framework that enables evaluation of this metric. The proposed metric is evaluated for different kinds of SO mechanisms in two case studies: a smart energy management system and a self-organizing production cell.
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- 1.
We use the term “prosumer” to refer to producers as well as consumers.
References
Anders, G., Seebach, H., Nafz, F., Steghöfer, J.P., Reif, W.: Decentralized reconfiguration for self-organizing resource-flow systems based on local knowledge. In: Proceedings of the 8th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2011), pp. 20–31. IEEE (2011)
Anders, G., Siefert, F., Reif, W.: A particle swarm optimizer for solving the set partitioning problem in the presence of partitioning constraints. In: Proceedings of the 7th International Conference on Agents & AI (ICAART) (2015)
Becker, M., Luckey, M., Becker, S.: Performance analysis of self-adaptive systems for requirements validation at design-time. In: 9th ACM SIGSOFT International Conference on Quality of Software Architectures (QoSA 2013). ACM (2013)
Belzner, L., Hölzl, M., Koch, N., Wirsing, M.: Collective autonomic systems: towards engineering principles and their foundations. In: Steffen, B. (ed.) Transactions on Foundations for Mastering Change I. LNCS, vol. 9960, pp. 180–200. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46508-1_10
Eberhardinger, B., Anders, G., Seebach, H., Siefert, F., Knapp, A., Reif, W.: An approach for isolated testing of self-organization algorithms. In: de Lemos, R., Garlan, D., Ghezzi, C., Giese, H. (eds.) Software Engineering for Self-Adaptive Systems III. Assurances. LNCS, vol. 9640, pp. 188–222. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-74183-3_7
Eberhardinger, B., Anders, G., Seebach, H., Siefert, F., Reif, W.: A research overview and evaluation of performance metrics for self-organization algorithms. In: Proceedings of the 9th International Conference on Self-Adaptive and Self-Organizing Systems Workshops, pp. 122–127. IEEE (2015)
Eberhardinger, B., Habermaier, A., Seebach, H., Reif, W.: Back-to-back testing of self-organization mechanisms. In: Wotawa, F., Nica, M., Kushik, N. (eds.) ICTSS 2016. LNCS, vol. 9976, pp. 18–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47443-4_2
Habermaier, A., Eberhardinger, B., Seebach, H., Leupolz, J., Reif, W.: Runtime model-based safety analysis of self-organizing systems with S#. In: 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW), pp. 128–133. IEEE (2015)
Kaddoum, E., Raibulet, C., Georgé, J., Picard, G., Gleizes, M.P.: Criteria for the evaluation of self-* systems. In: Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, pp. 29–38 (2010)
Kantert, J., Tomforde, S., Müller-Schloer, C., Edenhofer, S., Sick, B.: Quantitative robustness - a generalised approach to compare the impact of disturbances in self-organising systems. In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence, ICAART 2017, pp. 39–50 (2017)
McGeoch, C.: A Guide to Experimental Algorithmics. Cambridge University Press, Cambridge (2012)
Monostori, L.: Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17, 9–13 (2014)
Musa, J.D.: A theory of software reliability and its application. IEEE Trans. Softw. Eng. 1(3), 312–327 (1975)
Neyman, J.: Outline of a theory of statistical estimation based on the classical theory of probability. Phil. Trans. R. Soc. Lond. A 236(767), 333–380 (1937)
Parunak, H.V.D., Brueckner, S.A.: Software engineering for self-organizing systems. In: Proceedings of the 12th International Workshops on Agent-Oriented Software Engineering (AOSE 2011), pp. 1–22 (2011)
Pitt, J., Busquets, D., Riveret, R.: Procedural justice and ‘Fitness for Purpose’ of self-organising electronic institutions. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS (LNAI), vol. 8291, pp. 260–275. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44927-7_18
Reinecke, P., Wolter, K., Van Moorsel, A.: Evaluating the adaptivity of computing systems. Perform. Eval. 67(8), 676–693 (2010)
Steghöfer, J.P., Anders, G., Siefert, F., Reif, W.: A system of systems approach to the evolutionary transformation of power management systems. In: Proceedings of INFORMATIK - Workshops on Smart Grids. LNI. Köllen Verlag (2013)
Taranu, S., Tiemann, J.: On assessing self-adaptive systems. In: Proceedings of the 8th International Conference on Pervasive Computing and Communications Workshops, pp. 214–219. IEEE (2010)
Villegas, N.M., Müller, H.A., Tamura, G., Duchien, L., Casallas, R.: A framework for evaluating quality-driven self-adaptive software systems. In: Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 80–89. ACM (2011)
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This research is sponsored by the research project Testing self-organizing, adaptive Systems (TeSOS) of the German Research Foundation.
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Eberhardinger, B., Ponsar, H., Klumpp, D., Reif, W. (2018). Measuring and Evaluating the Performance of Self-Organization Mechanisms Within Collective Adaptive Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Distributed Systems. ISoLA 2018. Lecture Notes in Computer Science(), vol 11246. Springer, Cham. https://doi.org/10.1007/978-3-030-03424-5_14
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