Abstract
Non-functional properties of collective adaptive systems (CAS) are of paramount relevance practically in any application. This paper compares two recently proposed approaches to quantitative modelling that exploit different system abstractions: the first is based on generalised stochastic Petri nets, and the second is based on queueing networks. Through a case study involving autonomous robots, we analyse and discuss the relative merits of the approaches. This is done by considering three scenarios which differ on the architecture used to coordinate the distributed components. Our experimental results assess a high accuracy when comparing model-based performance analysis results derived from two different quantitative abstractions for CAS.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Abd Alrahman, Y., De Nicola, R., Loreti, M.: A calculus for collective-adaptive systems and its behavioural theory. Inf. Comput. 268, 104457 (2019)
Ali, A., Pinciroli, R., Yan, F., Smirni, E.: It’s not a sprint, it’s a marathon: stretching multi-resource burstable performance in public clouds. In: International Middleware Conference (Middleware) Industrial Track, pp. 36–42. ACM, New York (2019)
Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126(2), 183–235 (1994)
Apvrille, L., Tanzi, T., Dugelay, J.-L.: Autonomous drones for assisting rescue services within the context of natural disasters. In: URSI General Assembly and Scientific Symposium (URSI GASS), pp. 1–4 (2014)
Balbo, G., Ciardo, G.: On Petri nets in performance and reliability evaluation of discrete event dynamic systems. In: Carl Adam Petri: Ideas, Personality, Impact, pp. 173–185. Springer, Berlin (2019)
Bartoletti, M., Cimoli, T., Murgia, M., Podda, A.S., Pompianu, L.: A contract-oriented middleware. In: International Conference on Formal Aspects of Component Software (FACS), vol. 9539, pp. 86–104. Springer, Berlin (2015)
Bartoletti, M., Cimoli, T., Murgia, M.: Timed session types. Log. Methods Comput. Sci. 13(4) (2017)
Bertoli, M., Casale, G., Serazzi, G.: JMT: performance engineering tools for system modeling. ACM SIGMETRICS Perform. Eval. Rev. 36(4), 10–15 (2009)
Bocchi, L., Yang, W., Yoshida, N.: Timed multiparty session types. In: International Conference on Concurrency Theory (CONCUR), vol. 8704, pp. 419–434. Springer, Berlin (2014)
Bocchi, L., Murgia, M., Vasconcelos, V.T., Yoshida, N.: Asynchronous timed session types – from duality to time-sensitive processes. In: Programming Languages and Systems (ESOP), vol. 11423, pp. 583–610. Springer, Berlin (2019)
Castro-Perez, D., Yoshida, N.: CAMP: cost-aware multiparty session protocols. Proc. ACM Program. Lang. 4(OOPSLA), 155:1–155:30 (2020)
Cerotti, D., Gribaudo, M., Piazzolla, P., Pinciroli, R., Serazzi, G.: Multi-class queuing networks models for energy optimization. In: International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS). EAI (2014)
Cerotti, D., Gribaudo, M., Pinciroli, R., Serazzi, G.: Stochastic Analysis of Energy Consumption in Pool Depletion Systems. LNCS, vol. 9629, pp. 25–39. Springer, Berlin (2016)
Das, A., Hoffmann, J., Pfenning, F.: Parallel complexity analysis with temporal session types. Proc. ACM Program. Lang. 91, 1 (2018)
De Nicola, R., Jähnichen, S., Wirsing, M.: Rigorous engineering of collective adaptive systems. Int. J. Softw. Tools Technol. Transf. 22(4), 389–397 (2020)
Gribaudo, M., Pinciroli, R., Trivedi, K.S.: Epistemic uncertainty propagation in power models. Electron. Notes Theor. Comput. Sci. 337, 67–86 (2018)
Inverso, O., Melgratti, H.C., Padovani, L., Trubiani, C., Tuosto, E.: Probabilistic analysis of binary sessions. In: International Conference on Concurrency Theory (CONCUR). LIPIcs, vol. 171, pp. 14:1–14:21 (2020)
Inverso, O., Trubiani, C., Tuosto, E.: Abstractions for collective adaptive systems. In: International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA). Springer, Berlin (2020)
Johari, M.H., Jawaddi, S.N.A., Ismail, A.: Survey on formation verification for ensembling collective adaptive system. Adv. Data Comput. Commun. Secur., 219–228 (2022)
Lazowska, E., Zahorjan, J., Graham, G.S., Sevcik, K.: Computer System Analysis Using Queueing Network Models. Prentice Hall International, Englewood Cliffs (1984)
Lee, J., Bae, K., Ölveczky, P.C.: An extension of hybridsynchaadl and its application to collaborating autonomous uavs. In: Proceedings of International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA), pp. 47–64 (2022)
Lion, B., Arbab, F., Talcott, C.: A rewriting framework for interacting cyber-physical agents. In: Proceedings of International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA), pp. 356–372 (2022)
Lopes, L., Martins, F.: A safe-by-design programming language for wireless sensor networks. J. Syst. Archit. 63, 16–32 (2016)
López, H.A., Heussen, K.: Choreographing cyber-physical distributed control systems for the energy sector. In: SAC, pp. 437–443. ACM, New York (2017)
López, H., Nielson, F., Nielson, H.: Enforcing availability in failure-aware communicating systems. In: International Conference on Formal Techniques for Distributed Objects, Components, and Systems (FORTE), vol. 9688, pp. 195–211. Springer, Berlin (2016)
Loreti, M., Hillston, J.: Modelling and analysis of collective adaptive systems with carma and its tools. In: International School on Formal Methods for the Design of Computer, Communication and Software Systems, pp. 83–119. Springer, Berlin (2016)
Majumdar, R., Yoshida, N., Zufferey, D.: Multiparty motion coordination: from choreographies to robotics programs. Proc. ACM Program. Lang. 134, 1 (2020)
Murgia, M., Pinciroli, R., Trubiani, C., Tuosto, E.: On model-based performance analysis of collective adaptive systems. In: LNCS, vol. 13703, pp. 266–282. Springer, Berlin (2022)
Neykova, R., Bocchi, L., Yoshida, N.: Timed runtime monitoring for multiparty conversations. Form. Asp. Comput. 29(5), 877–910 (2017)
Pianini, D., Casadei, R., Viroli, M., Natali, A.: Partitioned integration and coordination via the self-organising coordination regions pattern. Future Gener. Comput. Syst. 114, 44–68 (2021)
Pinciroli, R., Trubiani, C.: Model-based performance analysis for architecting cyber-physical dynamic spaces. In: International Conference on Software Architecture (ICSA), pp. 104–114 (2021)
Pinciroli, R., Trubiani, C.: Performance analysis of fault-tolerant multi-agent coordination mechanisms. IEEE Trans. Ind. Inform., 1–12 (2023)
Pinciroli, R., Trivedi, K.S., Bobbio, A.: Parametric sensitivity and uncertainty propagation in dependability models. In: International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS). ACM, New York (2016)
Pinciroli, R., Ali, A., Yan, F., Smirni, E.: CEDULE+: resource management for burstable cloud instances using predictive analytics. IEEE Trans. Netw. Serv. Manag. 18(1), 945–957 (2021)
Pinciroli, R., Smith, C.U., Trubiani, C.: QN-based Modeling and Analysis of Software Performance Antipatterns for Cyber-Physical Systems. In: International Conference on Performance Engineering (ICPE), pp. 93–104. ACM, New York (2021)
Töpfer, M., Abdullah, M., Bureš, T., Hnětynka, P., Kruliš, M.: Ensemble-based modeling abstractions for modern self-optimizing systems. In: Proceedings of International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA), pp. 318–334 (2022)
Tuosto, E., Guanciale, R.: Semantics of global view of choreographies. J. Log. Algebraic Methods Program. 95, 17–40 (2018)
Vandin, A., Tribastone, M.: Quantitative abstractions for collective adaptive systems. In: International School on Formal Methods for the Design of Computer, Communication and Software Systems, pp. 202–232. Springer, Berlin (2016)
Viroli, M., Audrito, G., Beal, J., Damiani, F., Pianini, D.: Engineering resilient collective adaptive systems by self-stabilisation. ACM Trans. Model. Comput. Simul. 28(2), 1–28 (2018)
Weidinger, F., Boysen, N., Briskorn, D.: Storage assignment with rack-moving mobile robots in KIVA warehouses. Transp. Sci. 52(6), 1479–1495 (2018)
Acknowledgements
The authors thank the anonimous reviewers for their valuable feedback that helped to improve the quality of the paper.
Funding
Open access funding provided by Gran Sasso Science Institute – GSSI within the CRUI-CARE Agreement. Research partly supported by the EU H2020 RISE programme under the Marie Skłodowska-Curie grant agreement No 778233. We acknowledge the partial support of MUR project PRIN 20228FT78M DREAM (modular software design to reduce uncertainty in ethics-based cyber-physical systems), MUR project PRIN 2017FTXR7S IT MATTERS (Methods and Tools for Trustworthy Smart Systems), MUR project PNRR VITALITY (ECS00000041) Spoke 2 ASTRA – Advanced Space Technologies and Research Alliance, and the MUR project PON REACT EU DM 1062/21.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Murgia, M., Pinciroli, R., Trubiani, C. et al. Comparing perfomance abstractions for collective adaptive systems. Int J Softw Tools Technol Transfer 25, 785–798 (2023). https://doi.org/10.1007/s10009-023-00728-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10009-023-00728-9