Title
Designing Self-Aware Adaptive Systems: From Autonomic Computing to Cognitive Immune Networks.
Abstract
An autonomic system is composed of ensembles of heterogeneous autonomic components in which large sets of components are dynamically added and removed. Nodes within such an ensemble should cooperate to achieve system or human goals, and systems are expected to self-adapt with little or no human-interaction. Designing such systems poses significant challenges. In this paper we propose that the system engineer might gain significant inspiration by looking to the biological immune system, particularly by adopting a perspective on the immune system proposed by Cohen known as the Cognitive Immune Network. The goal of this paper is to show how the current literature in autonomic computing could be positively enriched by considering alternative design processes based on cognitive immune networks. After sketching out the mapping in commonalities between the Cognitive Immune Network and the autonomic computing reference model, we demonstrate how these considerations regarding the design process can be exploited with an engineered autonomic system by describing experiments with a simple robotic swarm scenario.
Year
DOI
Venue
2013
10.1109/SASOW.2013.17
Self-Adaptation and Self-Organizing Systems Workshops
Keywords
Field
DocType
cognitive immune network,autonomic computing reference model,biological immune system,autonomic system,cognitive immune networks,autonomic computing,engineered autonomic system,alternative design,system engineer,designing self-aware adaptive systems,immune system,heterogeneous autonomic component,adaptive systems,artificial immune systems
Autonomic computing,Artificial immune system,Swarm behaviour,Reference model,Computer science,Adaptive system,Real-time computing,Engineering design process,Immune system,Cognition,Distributed computing
Conference
Citations 
PageRank 
References 
4
0.49
10
Authors
3
Name
Order
Citations
PageRank
Nicola Capodieci18216.13
Emma Hart29718.02
Giacomo Cabri31018106.91