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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les approches d'inspiration biologique sont l'une des approches les plus prometteuses pour réaliser des systèmes distribués hautement adaptatifs. Les systèmes biologiques ont intrinsèquement des propriétés d’auto*, telles que l’auto-stabilisation, l’auto-adaptation, l’auto-configuration, l’auto-optimisation et l’auto-guérison. Ainsi, l’application de systèmes biologiques dans des systèmes distribués a récemment attiré beaucoup d’attention. Dans cet article, nous présentons un résultat réussi d'une approche bio-inspirée : nous proposons des algorithmes distribués pour la réplication des ressources inspirés du modèle de population d'une seule espèce. La réplication des ressources est une technique cruciale pour améliorer les performances du système des applications distribuées avec des ressources partagées. Dans les systèmes utilisant la réplication de ressources, en général, un plus grand nombre de répliques réduit le temps nécessaire pour atteindre une réplique d'une ressource demandée mais consomme plus de stockage des hôtes. Il est donc indispensable d’ajuster le nombre de répliques de manière appropriée pour l’application de partage de ressources. Cet article considère le problème du contrôle adaptatif des densités de répliques dans les réseaux dynamiques et propose deux algorithmes distribués bio-inspirés pour le problème. Dans le premier algorithme, nous essayons de contrôler la densité des répliques pour une seule ressource. Cependant, dans un système où coexistent plusieurs ressources, l’algorithme nécessite un coût de réseau élevé et une connaissance exacte à chaque nœud de toutes les ressources du réseau. Dans le deuxième algorithme, les densités de toutes les ressources sont contrôlées par un algorithme unique sans coût de réseau élevé et sans connaissance exacte de toutes les ressources. Cet article montre par simulations que ces deux algorithmes réalisent une auto-adaptation de la densité de répliques dans les réseaux dynamiques.
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Tomoko IZUMI, Taisuke IZUMI, Fukuhito OOSHITA, Hirotsugu KAKUGAWA, Toshimitsu MASUZAWA, "A Biologically Inspired Self-Adaptation of Replica Density Control" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 5, pp. 1125-1136, May 2009, doi: 10.1587/transinf.E92.D.1125.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1125/_p
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@ARTICLE{e92-d_5_1125,
author={Tomoko IZUMI, Taisuke IZUMI, Fukuhito OOSHITA, Hirotsugu KAKUGAWA, Toshimitsu MASUZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={A Biologically Inspired Self-Adaptation of Replica Density Control},
year={2009},
volume={E92-D},
number={5},
pages={1125-1136},
abstract={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.},
keywords={},
doi={10.1587/transinf.E92.D.1125},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - A Biologically Inspired Self-Adaptation of Replica Density Control
T2 - IEICE TRANSACTIONS on Information
SP - 1125
EP - 1136
AU - Tomoko IZUMI
AU - Taisuke IZUMI
AU - Fukuhito OOSHITA
AU - Hirotsugu KAKUGAWA
AU - Toshimitsu MASUZAWA
PY - 2009
DO - 10.1587/transinf.E92.D.1125
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2009
AB - 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.
ER -