The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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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
생물학적으로 영감을 받은 접근 방식은 고도로 적응력이 뛰어난 분산 시스템을 실현하는 가장 유망한 접근 방식 중 하나입니다. 생물학적 시스템은 본질적으로 자가 안정화, 자가 적응, 자가 구성, 자가 최적화 및 자가 치유와 같은 자가 특성을 가지고 있습니다. 따라서 최근 생물학적 시스템을 분산 시스템에 적용하는 것이 많은 주목을 받고 있다. 본 논문에서는 생물 영감을 받은 접근 방식의 성공적인 결과 중 하나를 제시합니다. 즉, 단일 종 개체군 모델에서 영감을 얻은 자원 복제를 위한 분산 알고리즘을 제안합니다. 리소스 복제는 공유 리소스를 사용하는 분산 애플리케이션의 시스템 성능을 향상시키는 데 중요한 기술입니다. 리소스 복제를 사용하는 시스템에서는 일반적으로 복제본 수가 많을수록 요청된 리소스의 복제본에 도달하는 데 걸리는 시간이 짧아지지만 호스트의 스토리지를 더 많이 소비합니다. 따라서 리소스 공유 애플리케이션에 맞게 복제본 수를 조정하는 것이 필수적입니다. 본 논문에서는 동적 네트워크에서 복제물의 밀도를 적응적으로 제어하는 문제를 고려하고 이 문제에 대한 두 가지 생체모방 분산 알고리즘을 제안합니다. 첫 번째 알고리즘에서는 단일 리소스에 대한 복제본 밀도를 제어하려고 합니다. 그러나 여러 자원이 공존하는 시스템에서 알고리즘은 높은 네트워크 비용과 네트워크의 모든 자원에 대한 각 노드의 정확한 지식이 필요합니다. 두 번째 알고리즘에서는 높은 네트워크 비용과 모든 리소스에 대한 정확한 지식 없이 단일 알고리즘으로 모든 리소스의 밀도를 제어합니다. 이 논문은 이 두 알고리즘이 동적 네트워크에서 복제본 밀도의 자체 적응을 실현한다는 것을 시뮬레이션을 통해 보여줍니다.
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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},}
부
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 -