A Biologically Inspired Self-Adaptation of Replica Density Control

Tomoko IZUMI  Taisuke IZUMI  Fukuhito OOSHITA  Hirotsugu KAKUGAWA  Toshimitsu MASUZAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.5   pp.1125-1136
Publication Date: 2009/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1125
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Distributed Cooperation and Agents
replica density control,  resource replication,  bio-inspired approach,  single species population model,  

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Biologically-inspired approaches are one of the most promising approaches to realize highly-adaptive distributed systems. Biological systems inherently have self-* properties, such as self-stabilization, self-adaptation, self-configuration, self-optimization and self-healing. Thus, the application of biological systems into distributed systems has attracted a lot of attention recently. In this paper, we present one successful result of bio-inspired approach: we propose distributed algorithms for resource replication inspired by the single species population model. Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. In systems using resource replication, generally, a larger number of replicas lead to shorter time to reach a replica of a requested resource but consume more storage of the hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for the resource sharing application. This paper considers the problem for controlling the densities of replicas adaptively in dynamic networks and proposes two bio-inspired distributed algorithms for the problem. In the first algorithm, we try to control the replica density for a single resource. However, in a system where multiple resources coexist, the algorithm needs high network cost and the exact knowledge at each node about all resources in the network. In the second algorithm, the densities of all resources are controlled by the single algorithm without high network cost and the exact knowledge about all resources. This paper shows by simulations that these two algorithms realize self-adaptation of the replica density in dynamic networks.