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Quantitative analysis of collective adaptive systems. (English) Zbl 06667334
Mazzara, Manuel (ed.) et al., Perspectives of system informatics. 10th international Andrei Ershov informatics conference, PSI 2015, in memory of Helmut Veith, Kazan and Innopolis, Russia, August 24–27, 2015. Revised selected papers. Cham: Springer (ISBN 978-3-319-41578-9/pbk; 978-3-319-41579-6/ebook). Lecture Notes in Computer Science 9609, 1-5 (2016).
Summary: Quantitative formal methods, such as stochastic process algebras, have been used for the last twenty years to support modelling of dynamic systems in order to investigate their performance. Application domains have ranged from computer and communication systems [1, 2], to intracellular signalling pathways in biological cells [3, 4]. Nevertheless this modelling approach is challenged by the demands of modelling modern collective adaptive systems, many of which have a strong spatial aspect, adding to the complexity of both the modelling and the analysis tasks.
For the entire collection see [Zbl 1347.68011].
68Qxx Theory of computing
Full Text: DOI
[1] Hermanns, H., Herzog, U., Katoen, J.: Process algebra for performance evaluation. Theor. Comput. Sci. 274(1–2), 43–87 (2002) · Zbl 0992.68149 · doi:10.1016/S0304-3975(00)00305-4
[2] De Nicola, R., Latella, D., Massink, M.: Formal modeling and quantitative analysis of klaim-based mobile systems. In: Proceedings of the 2005 ACM Symposium on Applied Computing (SAC), Santa Fe, New Mexico, USA, 13–17 March 2005, pp. 428–435. ACM (2005) · doi:10.1145/1066677.1066777
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[4] Ciocchetta, F., Hillston, J.: Bio-PEPA: a framework for the modelling and analysis of biological systems. Theor. Comput. Sci. 410(33), 3065–3084 (2009) · Zbl 1173.68041 · doi:10.1016/j.tcs.2009.02.037
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[7] Marsan, M.A., Conte, G., Balbo, G.: A class of generalized stochastic petri nets for the performance evaluation of multiprocessor systems. ACM Trans. Comput. Syst. 2(2), 93–122 (1984) · doi:10.1145/190.191
[8] Hillston, J.: A Compositional Approach to Performance Modelling. CUP, Cambridge (1995) · Zbl 0861.90121
[9] Bernardo, M., Gorrieri, R.: A tutorial on EMPA: a theory of concurrent processes with nondeterminism, priorities probabilities and time. Theor. Comput. Sci. 202(1–2), 1–54 (1998) · Zbl 0902.68075 · doi:10.1016/S0304-3975(97)00127-8
[10] Hermanns, H.: Interactive Markov Chains: The Quest for Quantified Quality. LNCS. Springer, Heidelberg (2002) · Zbl 1012.68142
[11] Hillston, J.: The benefits of sometimes not being discrete. In: Baldan, P., Gorla, D. (eds.) CONCUR 2014. LNCS, vol. 8704, pp. 7–22. Springer, Heidelberg (2014) · Zbl 1417.68131 · doi:10.1007/978-3-662-44584-6_2
[12] Bortolussi, L., Hillston, J.: Model checking single agent behaviours by fluid approximation. Inf. Comput. 242, 183–226 (2015) · Zbl 1317.68108 · doi:10.1016/j.ic.2015.03.002
[13] Hillston, J., Loreti, M.: Specification and analysis of open-ended systems with CARMA. In: Weyns, D., Michel, F. (eds.) E4MAS 2014. LNCS, vol. 9068, pp. 95–116. Springer, Heidelberg (2015) · doi:10.1007/978-3-319-23850-0_7
[14] Bortolussi, L., De Nicola, R., Galpin, V., Gilmore, S., Hillston, J., Latella, D., Loreti, M., Massink, M.: CARMA: collective adaptive resource-sharing Markovian agents. In: Bertrand, N., Tribastone, M. (eds.) Proceedings Thirteenth Workshop on Quantitative Aspects of Programming Languages and Systems, QAPL 2015. EPTCS, London, UK, 11th–12th April 2015, vol. 194, pp. 16–31 (2015) · doi:10.4204/EPTCS.194.2
[15] Bortolussi, L.: Hybrid behaviour of markov population models. Inf. Comput. 247, 37–86 (2016). CoRR abs/1211.1643 (2012) · Zbl 1336.68177 · doi:10.1016/j.ic.2015.12.001
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