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EnglishAggregate computing is a recently proposed framework to build CASs (collective adaptive systems) by focussing on direct programming of ensembles so as to abstract away from individual devices and their single interaction acts: This approach is shown to streamline the identification of highly reusable block components, and support reasoning about their resiliency properties. Following this paradigm, in this paper we present a framework for bridging the gap between the MAPE (Monitor-Analyse-Plan-Execute) loop of autonomic computing managers, and fully-distributed selforganising CASs. This is achieved by seeing the collection of M components of each agent as an aggregate, amenable to a direct specification as overall CAS Monitoring behaviour, and similarly for A, P and E. As a result, a self-organising CAS can be programmed by clearly separating the M, A, P, and E parts of it; though each is expressed in terms of a collective behaviour. The proposed approach is exemplified with an application scenario of crowd dispersal in a large-scale smart-mobility application.
| Titolo: | Combining self-organisation & autonomic computing in CASs with aggregate-MAPE |
| Autore/i: | VIROLI, MIRKO; Bucchiarone, Antonio; PIANINI, DANILO; Beal, Jacob |
| Autore/i Unibo: | |
| Anno: | 2016 |
| Titolo del libro: | Proceedings - IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016 |
| Pagina iniziale: | 186 |
| Pagina finale: | 191 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/FAS-W.2016.49 |
| Abstract: | Aggregate computing is a recently proposed framework to build CASs (collective adaptive systems) by focussing on direct programming of ensembles so as to abstract away from individual devices and their single interaction acts: This approach is shown to streamline the identification of highly reusable block components, and support reasoning about their resiliency properties. Following this paradigm, in this paper we present a framework for bridging the gap between the MAPE (Monitor-Analyse-Plan-Execute) loop of autonomic computing managers, and fully-distributed selforganising CASs. This is achieved by seeing the collection of M components of each agent as an aggregate, amenable to a direct specification as overall CAS Monitoring behaviour, and similarly for A, P and E. As a result, a self-organising CAS can be programmed by clearly separating the M, A, P, and E parts of it; though each is expressed in terms of a collective behaviour. The proposed approach is exemplified with an application scenario of crowd dispersal in a large-scale smart-mobility application. |
| Data stato definitivo: | 17-mag-2017 |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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http://hdl.handle.net/11585/588361
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