Hillston, Jane

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

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     Edinburgh Research Explorer                                      Quantitative Analysis of Collective Adaptive SystemsCitation for published version:Hillston, J 2016, Quantitative Analysis of Collective Adaptive Systems. in Perspectives of SystemInformatics: 10th International Andrei Ershov Informatics Conference, PSI 2015, in Memory of Helmut Veith,Kazan and Innopolis, Russia, August 24-27, 2015, Revised Selected Papers. Lecture Notes in ComputerScience, vol. 9609, Springer International Publishing, pp. 1-5, 10th International Andrei Ershov InformaticsConference, Kazan, Russian Federation, 24/08/15. DOI: 10.1007/978-3-319-41579-6_1Digital Object Identifier (DOI):10.1007/978-3-319-41579-6_1Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublished In:Perspectives of System InformaticsGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Quantitative Analysis of Collective Adaptive SystemsJane HillstonLFCS, School of Informatics, University of Edinburghjane.hillston@ed.ac.ukhttp://www.quanticol.eu1 IntroductionQuantitative formal methods, such as stochastic process algebras, have been used forthe last twenty years to support modelling of dynamic systems in order to investigatetheir performance. Application domains have ranged from computer and communica-tion systems [1, 2], to intracellular signalling pathways in biological cells [3, 4]. Nev-ertheless this modelling approach is challenged by the demands of modelling moderncollective adaptive systems, many of which have a strong spatial aspect, adding to thecomplexity of both the modelling and the analysis tasks.In this talk I gave an introduction to formal quantitative analysis and the challengesof modelling collective adaptive systems, together with recent developments to addressthose challenges using the modelling language CARMA.2 Quantitative AnalysisPerformance analysis has a long tradition in computer and communication engineer-ing dating back to the 1960s, as the dynamic behaviour of systems can sometimes becounter-intuitive and hard to predict without detailed mathematical models. Since manyaspects of the system must be abstracted in order to construct tractable models, proba-bility distributions are used to represent the variability within the timing characteristicsof the system, for example, due to different data characteristics. Specifically continuoustime Markov chains (CTMCs) were found to offer a good compromise between faith-fulness and tractability. Initially queueing networks [5] were the dominant approach tocapturing the conflict for resources which is often at the root of performance problems.From such descriptions it is easy to build a CTMC, typically with a simple birth-deathprocess for each queue, but in many cases this is not even necessary as analytical so-lutions are known, circumventing the need for the explicit construction and analysis ofthe CTMC [6].However, the advent of large distributed systems, in which multiple resources maybe needed by processes simultaneously, led to the use of more flexible modelling frame-works such as stochastic Petri nets [7] and stochastic process algebras [8]. Stochasticprocess algebras are small textual description languages which represent a system asa number of interacting processes. At the basic level each process is a small CTMCcapturing the ordering and (stochastic) timing of activities that the process may under-take. The construction of the model specifies how these processes are constrained to2 Jane Hillstoninteract through shared activities. A large CTMC capturing the complete behaviour ofthe system can be automatically constructed from the stochastic process algebra de-scription, allowing the modeller to focus on the higher level behaviour of the systemrather than the underlying state space. Prime examples of stochastic process algebrasinclude PEPA [8], EMPA [9] and IMC [10]. Note, however, that the system descrip-tion and the underlying mathematical model (CTMC) are inherently discrete and thiscan pose significant challenges for representing large scale systems due to the problemof state space explosion. In particular, numerical solution to find the probability dis-tribution over the state space becomes intractable, and stochastic simulation becomescomputationally expensive, as the size of the system grows.3 Challenges in Modelling Collective Adaptive SystemsRecent years have seen increasing interest in collective adaptive systems. Such sys-tems, which appear in many natural scenarios such as the behaviour of social insects,are increasingly forming a paradigm for the construction of the software systems of thefuture. Collective adaptive systems (CAS) are seen to be comprised of a large num-ber of interacting entities whose behaviour is based on their local perception, withoutaccess to global control or knowledge. Moreover entities are typically replicated to es-tablish robustness to failure of individual entities whilst adaptivity provides robustnessto changes at a higher level.The global or emergent behaviour of such systems can be difficult to predict mak-ing it paramount that we develop adequate modelling formalisms to capture and reasonabout the behaviour at both local and global levels. The compositional nature of stochas-tic process algebras make them strong candidates for developing models at the locallevel, but the state space explosion problem places severe challenges on their analysis.In the last decade there have been efforts to alleviate the problems of state spaceexplosion for very large systems by the use of fluid approximations [11]. In this ap-proach an approximation of the underlying discrete CTMC is constructed as a set ofordinary differential equations that capture the average behaviour of the system when alarge population of entities is involved. Analysis techniques, such as stochastic modelchecking, have been adapted to work with this approximation [12].Other significant challenges for stochastic process algebras when modelling CASstem from the spatially distributed nature of the entities and the adaptation. The spatialaspect is important because entities are restricted to interact and communicate locally socapturing the relative positions of entities is crucial. Similarly, the ability of an entity tohave a goal which guides changes in behaviour based on the information that it receiveshas not previously been considered in stochastic process algebras. To address thesechallenges we have developed a new process algebra-based language CARMA [13].4 CARMACARMA has been designed specifically to represent systems developed according to theCAS paradigm [14]. The language offers a rich set of communication primitives, andpermits exploiting attributes, captured in a store associated with each component, toQuantitative Analysis of CAS 3enable attribute-based communication. For most CAS systems we anticipate that oneof the attributes could be the location of the agent. Thus it is straightforward to modelthose systems in which, for example, there is a limited scope of communication or thereis the restriction to only interact with components that are co-located, or where there isspatial heterogeneity in the behaviour of agents. The use of a store to explicitly capturea limited set of data associated with an entity is a compromise between full agent-basedmodelling and the approach of data abstraction that has previously been adopted instochastic process algebra-based languages.The rich set of communication primitives is one of the distinctive features of CARMA.Specifically, CARMA supports both unicast and broadcast communication, and permitslocally synchronous, but globally asynchronous communication. This richness is im-portant to take into account the spatially distributed nature of CAS, where agents mayhave only local awareness of the system, yet the design objectives and adaptation goalsare often expressed in terms of global behaviour. Representing these patterns of com-munication in classical process algebras or traditional stochastic process algebras wouldbe difficult, and would require the introduction of additional model components to rep-resent buffers, queues and other communication structures.Another key feature of CARMA is its distinct treatment of the environment. It shouldbe stressed that although this is an entity explicitly introduced within our models, it isintended to represent something more pervasive and diffusive of the real system, whichis abstracted within the modelling to be an entity which exercises influence and imposesconstraints on the different agents in the system. For example, in a model of a smarttransport system, the environment may have responsibility for determining the rate atwhich entities (buses, bikes, taxis etc) move through the city. However this should berecognised as an abstraction of the presence of other vehicles causing congestion whichmay impede the progress of the focus entities to a greater or lesser extent at differenttimes of the day. The presence of an environment in the model does not imply theexistence of centralised control in the system. The role of the environment is also relatedto the spatially distributed nature of CAS — we expect that the location where an agentis will have an effect on what an agent can do.To summarise, in CARMA a system is composed of a collective of components thatexist in an environment. Each component consists of a process and a store where theprocess captures the possible behaviours of the component in a similar manner to previ-ous process algebras, whereas the store records the state of the component with respectto a number of attributes. These attributes, which can be thought of as enumeratedtypes, allow the behaviour, including the communication partners, of a component tobe dependent on its current state.5 Future PerspectivesCAS present an interesting and challenging class of systems to design and construct,with many exciting prospects for future software systems as well as socio-technicalsystems, in which users themselves become entities in the system. Example areas ofapplication include smart urban transport systems, smart energy networks and swarmrobotics. The role of CAS within infrastructure systems, such as within smart cities,4 Jane Hillstonmake it essential that quantitive aspects of behaviour, in addition to functional correct-ness, are taken into consideration during design, but the scale and complexity of thesesystems pose challenges both for model construction and model analysis. CARMA aimsto address many of these challenges, supporting rich forms of interaction, using at-tributes to capture explicit locations and the environment to allow adaptivity. Moreoveranalysis techniques based on fluid approximation offer hope for scalable quantitativeanalysis techniques. However, to tackle the full range of behaviours which can occurwithin CAS extensions to classical fluid approximation techniques are needed. For ex-ample, recent work by Bortolussi [15] proves convergence results in terms of hybridsystems which take into account multi-scale behaviour with respect to time and/or pop-ulations.AcknowledgementThis work is partially supported by the EU project QUANTICOL, 600708.References1. Hermanns, H., Herzog, U., Katoen, J.: Process algebra for performance evaluation. Theor.Comput. Sci. 274(1-2) (2002) 43–872. De Nicola, R., Latella, D., Massink, M.: Formal modeling and quantitative analysis ofklaim-based mobile systems. In: Proc. of the 2005 ACM Symposium on Applied Computing(SAC), Santa Fe, New Mexico, USA, March 13-17, 2005, ACM (2005) 428–4353. Priami, C.: Algorithmic systems biology. Commun. ACM 52(5) (2009) 80–884. Ciocchetta, F., Hillston, J.: Bio-PEPA: A framework for the modelling and analysis of bio-logical systems. Theoretical Computer Science 410(33) (2009) 3065–30845. Kleinrock, L.: Sequential processing machines (S.P.M) analyzed with a queuing theorymodel. J. ACM 13(2) (1966) 179–1936. Baskett, F., Chandy, K.M., Muntz, R.R., Palacios, F.G.: Open, closed, and mixed networksof queues with different classes of customers. J. ACM 22(2) (1975) 248–2607. Marsan, M.A., Conte, G., Balbo, G.: A class of generalized stochastic petri nets for theperformance evaluation of multiprocessor systems. ACM Trans. Comput. Syst. 2(2) (1984)93–1228. Hillston, J.: A Compositional Approach to Performance Modelling. CUP (1995)9. Bernardo, M., Gorrieri, R.: A Tutorial on EMPA: A Theory of Concurrent Processes withNondeterminism, Priorities, Probabilities and Time. Theoretical Computer Science 202(1-2)(1998) 1–5410. Hermanns, H.: Interactive Markov Chains: The Quest for Quantified Quality. LNCS.Springer (202)11. Hillston, J.: The benefits of sometimes not being discrete. In Baldan, P., Gorla, D., eds.:CONCUR 2014 - Concurrency Theory - 25th International Conference, CONCUR 2014,Rome, Italy, September 2-5, 2014. Proceedings. Volume 8704 of Lecture Notes in ComputerScience., Springer (2014) 7–2212. Bortolussi, L., Hillston, J.: Model checking single agent behaviours by fluid approximation.Inf. Comput. 242 (2015) 183–226Quantitative Analysis of CAS 513. Hillston, J., Loreti, M.: Specification and analysis of open-ended systems with CARMA.In Weyns, D., Michel, F., eds.: Agent Environments for Multi-Agent Systems IV - 4th In-ternational Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May 6, 2014, RevisedSelected and Invited Papers. Volume 9068 of Lecture Notes in Computer Science., Springer(2014) 95–11614. 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 Pro-gramming Languages and Systems, QAPL 2015, London, UK, 11th-12th April 2015. Vol-ume 194 of EPTCS. (2015) 16–3115. Bortolussi, L.: Hybrid behaviour of markov population models. CoRR abs/1211.1643 (2012)to appear in Information and Computation 2016.

Quantitative Analysis of Collective Adaptive Systems

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Quantitative Analysis of Collective Adaptive Systems

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