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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pendekatan yang diilhamkan secara biologi adalah salah satu pendekatan yang paling menjanjikan untuk merealisasikan sistem teragih yang sangat mudah suai. Sistem biologi sememangnya mempunyai sifat-* diri, seperti penstabilan diri, penyesuaian diri, konfigurasi diri, pengoptimuman diri dan penyembuhan diri. Oleh itu, aplikasi sistem biologi ke dalam sistem teragih telah menarik banyak perhatian baru-baru ini. Dalam makalah ini, kami membentangkan satu hasil yang berjaya dari pendekatan bio-inspirasi: kami mencadangkan algoritma yang diedarkan untuk replikasi sumber yang diilhamkan oleh model populasi spesies tunggal. Replikasi sumber ialah teknik penting untuk meningkatkan prestasi sistem aplikasi yang diedarkan dengan sumber yang dikongsi. Dalam sistem yang menggunakan replikasi sumber, secara amnya, bilangan replika yang lebih besar membawa kepada masa yang lebih singkat untuk mencapai replika sumber yang diminta tetapi menggunakan lebih banyak storan hos. Oleh itu, adalah amat diperlukan untuk melaraskan bilangan replika dengan sewajarnya untuk aplikasi perkongsian sumber. Makalah ini mempertimbangkan masalah untuk mengawal ketumpatan replika secara adaptif dalam rangkaian dinamik dan mencadangkan dua algoritma teragih bio-inspirasi untuk masalah tersebut. Dalam algoritma pertama, kami cuba mengawal ketumpatan replika untuk satu sumber. Walau bagaimanapun, dalam sistem yang berbilang sumber wujud bersama, algoritma memerlukan kos rangkaian yang tinggi dan pengetahuan yang tepat pada setiap nod tentang semua sumber dalam rangkaian. Dalam algoritma kedua, ketumpatan semua sumber dikawal oleh algoritma tunggal tanpa kos rangkaian yang tinggi dan pengetahuan yang tepat tentang semua sumber. Makalah ini menunjukkan melalui simulasi bahawa kedua-dua algoritma ini menyedari penyesuaian diri ketumpatan replika dalam rangkaian dinamik.
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Salinan
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
Salinan
@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},}
Salinan
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 -