Abstract:Similar to the Autonomous Computing initiative, which has mainly been advancing techniques for self-optimization focusing on computing systems and infrastructures, Organic Computing (OC) has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of au… Show more
“…Since the 2000s and earlier, research mainly stemming from the fields of coordination [4], multi-agent systems [5], selforganisation [6], [7], and swarm robotics [8] has proposed abstractions and mechanisms to engineer and program CASs. These include e.g.…”
Section: Introductionmentioning
confidence: 99%
“…These include e.g. spatial abstractions [9], macro-programming [10], [11], field-based coordination [12], and ensemblebased approaches [7], [13], [14]. A leitmotiv in these proposals is the definition of ways to capture dynamic aggregates (or ensembles), namely groups of devices that change at runtime and model providers for inputs, executors of collective tasks, recipient for multicast communications, and so on.…”
Section: Introductionmentioning
confidence: 99%
“…A leitmotiv in these proposals is the definition of ways to capture dynamic aggregates (or ensembles), namely groups of devices that change at runtime and model providers for inputs, executors of collective tasks, recipient for multicast communications, and so on. These ensembles have proven to be crucial to promote desired collective and self-organising behaviours [7], [15], [13].…”
Engineering and programming approaches for collective adaptive systems often leverage ensemble-or group-like abstractions to characterise a subset of devices as a domain for a given task or computation. In this paper, we address the problem of programming the dynamic evolution of distributed computational aggregates, through neighbour-based coordination. This is a problem of interest, since several situated activities -especially in large-scale settings -require decentralised collaboration, and need to be sustained by limited subsets of devices. These subsets may vary dynamically due to delegation, completion of local contributions, exhaustion of resources, failure, or change in the device set induced by the openness of system boundaries.In order to formally study and develop how distributed aggregates progressively take form by local coordination, we build on the field-based framework of aggregate processes, and extend it with techniques to support more expressive evolution dynamics. We propose novel algorithms for more effective propagation and closure of the boundaries of dynamic aggregates, based on statistics on the information speed and a notion of progressive closure through wave-like propagation. We verify the proposed techniques by simulation of a paradigmatic case study of multihop message delivery in mobile settings, and show increased performance and success rate with respect to previous work.
“…Since the 2000s and earlier, research mainly stemming from the fields of coordination [4], multi-agent systems [5], selforganisation [6], [7], and swarm robotics [8] has proposed abstractions and mechanisms to engineer and program CASs. These include e.g.…”
Section: Introductionmentioning
confidence: 99%
“…These include e.g. spatial abstractions [9], macro-programming [10], [11], field-based coordination [12], and ensemblebased approaches [7], [13], [14]. A leitmotiv in these proposals is the definition of ways to capture dynamic aggregates (or ensembles), namely groups of devices that change at runtime and model providers for inputs, executors of collective tasks, recipient for multicast communications, and so on.…”
Section: Introductionmentioning
confidence: 99%
“…A leitmotiv in these proposals is the definition of ways to capture dynamic aggregates (or ensembles), namely groups of devices that change at runtime and model providers for inputs, executors of collective tasks, recipient for multicast communications, and so on. These ensembles have proven to be crucial to promote desired collective and self-organising behaviours [7], [15], [13].…”
Engineering and programming approaches for collective adaptive systems often leverage ensemble-or group-like abstractions to characterise a subset of devices as a domain for a given task or computation. In this paper, we address the problem of programming the dynamic evolution of distributed computational aggregates, through neighbour-based coordination. This is a problem of interest, since several situated activities -especially in large-scale settings -require decentralised collaboration, and need to be sustained by limited subsets of devices. These subsets may vary dynamically due to delegation, completion of local contributions, exhaustion of resources, failure, or change in the device set induced by the openness of system boundaries.In order to formally study and develop how distributed aggregates progressively take form by local coordination, we build on the field-based framework of aggregate processes, and extend it with techniques to support more expressive evolution dynamics. We propose novel algorithms for more effective propagation and closure of the boundaries of dynamic aggregates, based on statistics on the information speed and a notion of progressive closure through wave-like propagation. We verify the proposed techniques by simulation of a paradigmatic case study of multihop message delivery in mobile settings, and show increased performance and success rate with respect to previous work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.