3d International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems

Research Article

Applying Self-Aggregation to Load Balancing: Experimental Results

Download237 downloads
  • @INPROCEEDINGS{10.4108/ICST.BIONETICS2008.4693,
        author={Elisabetta Di Nitto and Daniel J. Dubois and Raffaela Mirandola and Fabrice Saffre and Richard Tateson},
        title={Applying Self-Aggregation to Load Balancing: Experimental Results},
        proceedings={3d International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems},
        publisher={ICST},
        proceedings_a={BIONETICS},
        year={2010},
        month={5},
        keywords={Autonomic computing self-organizing systems load balancing clustering performance analysis},
        doi={10.4108/ICST.BIONETICS2008.4693}
    }
    
  • Elisabetta Di Nitto
    Daniel J. Dubois
    Raffaela Mirandola
    Fabrice Saffre
    Richard Tateson
    Year: 2010
    Applying Self-Aggregation to Load Balancing: Experimental Results
    BIONETICS
    ICST
    DOI: 10.4108/ICST.BIONETICS2008.4693
Elisabetta Di Nitto1,*, Daniel J. Dubois1,*, Raffaela Mirandola1,*, Fabrice Saffre2,*, Richard Tateson2,*
  • 1: Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy.
  • 2: BT Group, Adastral Park, Ipswitch IP5 3RE, UK
*Contact email: dinitto@elet.polimi.it, dubois@elet.polimi.it, mirandola@elet.polimi.it, fabrice.saffre@bt.com, richard.tateson@bt.com

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 this context a crucial role is played by the balancing of the work load among all nodes in the system. So far load balancing approaches have been designed for networks with fixed or dynamic topologies. These approaches work well in the case each node knows its similes and is able to contact them to delegate tasks. However, they do not address the needs of more dynamic systems where nodes are able to process different types of jobs and have limited knowledge about their neighbors and the whole system. To address these issue, we are experimenting with the usage of autonomic self-aggregation techniques that rewire such highly dynamic systems in groups of homogeneous nodes that are then able to balance the load among each others. We present our approach and show through simulation that it provides significant advantages under the circumstances described before.