Research Article
Self-Aggregation Algorithms for Autonomic Systems
@INPROCEEDINGS{10.4108/ICST.BIONETICS2007.2411, author={Elisabetta Di Nitto and Daniele J. Dubois and Raffaela Mirandola}, title={Self-Aggregation Algorithms for Autonomic Systems}, proceedings={2nd International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems}, proceedings_a={BIONETICS}, year={2008}, month={8}, keywords={Autonomic computing clustering algorithms distributed and self-adaptable systems performance analysis}, doi={10.4108/ICST.BIONETICS2007.2411} }- Elisabetta Di Nitto
Daniele J. Dubois
Raffaela Mirandola
Year: 2008
Self-Aggregation Algorithms for Autonomic Systems
BIONETICS
ICST
DOI: 10.4108/ICST.BIONETICS2007.2411
Abstract
One of the today issues in software engineering is to find new effective ways to deal intelligently with the increasing complexity of distributed computing systems. In particular, one of the aspects under study in the field of autonomic computing concerns the way such systems can autonomously reach a configuration that allows the entire system to work in a more efficient and effective way. In this paper we investigate how it is possible to obtain self-aggregation of distributed components. We have used existing self-aggregation algorithms as a starting point, and, after an analysis phase, we have discovered some aspects that could be improved. Finally we have derived new algorithms that showed improved self-aggregating performances in most of the situations.
