Abstract
This paper fosters the analysis of performance properties of collective adaptive systems (CAS) since such properties are of paramount relevance practically in any application. We compare two recently proposed approaches: the first is based on generalised stochastic petri nets derived from the system specification; the second is based on queueing networks derived from suitable behavioural abstractions. We use a case study based on a scenario involving autonomous robots to discuss the relative merit of the approaches. Our experimental results assess a mean absolute percentage error lower than 4% when comparing model-based performance analysis results derived from two different quantitative abstractions for CAS.
Work partly funded by MIUR PRIN projects 2017FTXR7S IT MATTERS (Methods and Tools for Trustworthy Smart Systems) and 2017TWRCNB SEDUCE (Designing Spatially Distributed Cyber-Physical Systems under Uncertainty).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We do not consider forking and joining points of parallel composition (represented by -gates) since this feature is not used in our case study.
- 2.
The fact that identifiers are unique is not built-in in our model; in principle there could be more doors with the same identifier.
References
Abd Alrahman, Y., De Nicola, R., Loreti, M.: A calculus for collective-adaptive systems and its behavioural theory. Inf. Comput. 268, 104457 (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: Reisig, W., Rozenberg, G. (eds.) Carl Adam Petri: Ideas, Personality, Impact, pp. 173–185. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96154-5_22
Bartoletti, M., Cimoli, T., Murgia, M.: Timed session types. Log. Methods Comput. Sci. 13(4) (2017)
Bartoletti, M., Cimoli, T., Murgia, M., Podda, A.S., Pompianu, L.: A contract-oriented middleware. In: Braga, C., Ölveczky, P.C. (eds.) FACS 2015. LNCS, vol. 9539, pp. 86–104. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28934-2_5
Bertoli, M., Casale, G., Serazzi, G.: JMT: performance engineering tools for system modeling. SIGMETRICS Perform. Evalu. Rev. 36(4), 10–15 (2009)
Bocchi, L., Murgia, M., Vasconcelos, V.T., Yoshida, N.: Asynchronous timed session types. In: Caires, L. (ed.) ESOP 2019. LNCS, vol. 11423, pp. 583–610. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17184-1_21
Bocchi, L., Yang, W., Yoshida, N.: Timed multiparty session types. In: Baldan, P., Gorla, D. (eds.) CONCUR 2014. LNCS, vol. 8704, pp. 419–434. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44584-6_29
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)
Das, A., Hoffmann, J., Pfenning, F.: Parallel complexity analysis with temporal session types. Proc. ACM Program. Lang. 2(ICFP), 91:1–91:30 (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), volume 171 of LIPIcs, pp. 14:1–14:21 (2020)
Inverso, O., Trubiani, C., Tuosto, E.: Abstractions for collective adaptive systems. In: Margaria, T., Steffen, B. (eds.) ISoLA 2020. LNCS, vol. 12477, pp. 243–260. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61470-6_15
Johari, M.H., Jawaddi, S.N.A., Ismail, A.: Survey on formation verification for ensembling collective adaptive system. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds.) Advances in Data Computing, Communication and Security. LNDECT, vol. 106, pp. 219–228. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-8403-6_19
Lazowska, E., Zahorjan, J., Scott Graham, G., Sevcik, K.: Computer System Analysis Using Queueing Network Models. Prentice-Hall Inc., Englewood Cliffs (1984)
Lazowska, E.D., Zahorjan, J., Graham, G.S., Sevcik, K.C.: Quantitative System Performance - Computer System Analysis Using Queueing Network Models. Prentice Hall, Englewood Cliffs (1984)
Lopes, L., Martins, F.: A safe-by-design programming language for wireless sensor networks. J. Syst. Archit. 63, 16–32 (2016)
López, H.A., Nielson, F., Nielson, H.R.: Enforcing availability in failure-aware communicating systems. In: Albert, E., Lanese, I. (eds.) FORTE 2016. LNCS, vol. 9688, pp. 195–211. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39570-8_13
López, H.A., Heussen, K.: Choreographing cyber-physical distributed control systems for the energy sector. In: SAC, pp. 437–443. ACM (2017)
Loreti, M., Hillston, J.: Modelling and analysis of collective adaptive systems with CARMA and its tools. In: Bernardo, M., De Nicola, R., Hillston, J. (eds.) SFM 2016. LNCS, vol. 9700, pp. 83–119. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34096-8_4
Majumdar, R., Yoshida, N., Zufferey, D.: Multiparty motion coordination: from choreographies to robotics programs. Proc. ACM Program. Lang. 4(OOPSLA), 134:1–134:30 (2020)
Neykova, R., Bocchi, L., Yoshida, N.: Timed runtime monitoring for multiparty conversations. Formal Aspects Comput. 29(5), 877–910 (2017). https://doi.org/10.1007/s00165-017-0420-8
Pianini, D., Casadei, R., Viroli, M., Natali, A.: Partitioned integration and coordination via the self-organising coordination regions pattern. Futur. Gener. Comput. Syst. 114, 44–68 (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 (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)
Tuosto, E., Guanciale, R.: Semantics of global view of choreographies. J. Log. Algebr. Meth. Program. 95, 17–40 (2018)
Vandin, A., Tribastone, M.: Quantitative abstractions for collective adaptive systems. In: Bernardo, M., De Nicola, R., Hillston, J. (eds.) SFM 2016. LNCS, vol. 9700, pp. 202–232. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34096-8_7
Viroli, M., Audrito, G., Beal, J., Damiani, F., Pianini, D.: Engineering resilient collective adaptive systems by self-stabilisation. ACM Trans. Model. Comput. Simul. (TOMACS) 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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Murgia, M., Pinciroli, R., Trubiani, C., Tuosto, E. (2022). On Model-Based Performance Analysis of Collective Adaptive Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-19759-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19758-1
Online ISBN: 978-3-031-19759-8
eBook Packages: Computer ScienceComputer Science (R0)