APICe » Publications » Combining Self-Organisation and Autonomic Computing in CASs with Aggregate-MAPE

Combining Self-Organisation and Autonomic Computing in CASs with Aggregate-MAPE

Mirko Viroli, Antonio Bucchiarone, Danilo Pianini, Jacob Beal
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.
2016 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASO Workshops 2016, Augsburg, Germany, September 18-22, 2016,
@article{,
	booktitle = {2016 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASO Workshops 2016, Augsburg, Germany, September 18-22, 2016},
	status = {In press},
	venue_list = {--},
	venue_s = {1st eCAS Workshop on Engineering Collective Adaptive Systems},
	author = {Viroli, Mirko AND Bucchiarone, Antonio AND Pianini, Danilo AND Beal, Jacob},
	title = {Combining Self-Organisation and Autonomic Computing in CASs with Aggregate-MAPE},
	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.},
	venue_e = {Events.Saso2016}}