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
인간 신체의 자연스러운 자기 관리 행동에서 영감을 받은 자율 시스템은 소프트웨어 시스템에 자기 관리 행동을 주입할 것을 약속합니다. 이러한 동작을 통해 소프트웨어 시스템의 자가 구성, 자가 치유, 자가 최적화 및 자가 보호 기능이 가능해집니다. 실시간 실행 환경과 같이 효율성이 핵심 문제인 시스템에서는 자체 구성이 필요합니다. 자율 시스템의 자기 구성 문제를 해결하기 위해 사례 기반 추론을 포함한 다양한 문제 해결 기술의 사용이 문헌에서 보고되었습니다. 사례 기반 추론 접근 방식은 자율 능력을 달성하는 데 도움이 될 수 있는 과거 경험을 활용합니다. 사례 형태의 사례 기반에 더 많은 경험이 추가됨에 따라 학습 과정이 향상됩니다. 이로 인해 케이스 기반이 더 커집니다. 사례 기반이 클수록 계산 비용 측면에서 효율성이 감소합니다. 이러한 효율성 문제를 극복하기 위해 본 논문에서는 보고된 문제의 해결책을 찾기 위해 사례 기반을 클러스터링하는 것을 제안합니다. 이 접근 방식은 새 사례를 사례 기반의 관련 클러스터로 제한하여 검색 복잡성을 줄입니다. 사례 기반 클러스터링은 일회성 프로세스이므로 정기적으로 반복할 필요가 없습니다. 본 문서에서 제안된 접근 방식은 새로운 클러스터형 CBR 프레임워크의 형태로 설명되었습니다. 제안된 프레임워크는 AFFA(Autonomic Forest Fire Application) 시뮬레이션에서 평가되었습니다. 이 문서에서는 시뮬레이션된 AFFA의 개요와 제안된 프레임워크에서 사례 기반을 클러스터링하기 위한 세 가지 다른 클러스터링 알고리즘에 대한 결과를 제시합니다. 기존 CBR 접근 방식과 클러스터형 CBR 접근 방식의 성능 비교는 ARP(Accuracy, Recall and Precision) 및 계산 효율성 측면에서 제시되었습니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Malik Jahan KHAN, Mian Muhammad AWAIS, Shafay SHAMAIL, "Improving Efficiency of Self-Configurable Autonomic Systems Using Clustered CBR Approach" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 11, pp. 3005-3016, November 2010, doi: 10.1587/transinf.E93.D.3005.
Abstract: Inspired from natural self-managing behavior of the human body, autonomic systems promise to inject self-managing behavior in software systems. Such behavior enables self-configuration, self-healing, self-optimization and self-protection capabilities in software systems. Self-configuration is required in systems where efficiency is the key issue, such as real time execution environments. To solve self-configuration problems in autonomic systems, the use of various problem-solving techniques has been reported in the literature including case-based reasoning. The case-based reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities. The learning process improves as more experience is added in the case-base in the form of cases. This results in a larger case-base. A larger case-base reduces the efficiency in terms of computational cost. To overcome this efficiency problem, this paper suggests to cluster the case-base, subsequent to find the solution of the reported problem. This approach reduces the search complexity by confining a new case to a relevant cluster in the case-base. Clustering the case-base is a one-time process and does not need to be repeated regularly. The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework. The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA). This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case-base in the proposed framework. The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy, Recall and Precision (ARP) and computational efficiency.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3005/_p
부
@ARTICLE{e93-d_11_3005,
author={Malik Jahan KHAN, Mian Muhammad AWAIS, Shafay SHAMAIL, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Efficiency of Self-Configurable Autonomic Systems Using Clustered CBR Approach},
year={2010},
volume={E93-D},
number={11},
pages={3005-3016},
abstract={Inspired from natural self-managing behavior of the human body, autonomic systems promise to inject self-managing behavior in software systems. Such behavior enables self-configuration, self-healing, self-optimization and self-protection capabilities in software systems. Self-configuration is required in systems where efficiency is the key issue, such as real time execution environments. To solve self-configuration problems in autonomic systems, the use of various problem-solving techniques has been reported in the literature including case-based reasoning. The case-based reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities. The learning process improves as more experience is added in the case-base in the form of cases. This results in a larger case-base. A larger case-base reduces the efficiency in terms of computational cost. To overcome this efficiency problem, this paper suggests to cluster the case-base, subsequent to find the solution of the reported problem. This approach reduces the search complexity by confining a new case to a relevant cluster in the case-base. Clustering the case-base is a one-time process and does not need to be repeated regularly. The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework. The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA). This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case-base in the proposed framework. The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy, Recall and Precision (ARP) and computational efficiency.},
keywords={},
doi={10.1587/transinf.E93.D.3005},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - Improving Efficiency of Self-Configurable Autonomic Systems Using Clustered CBR Approach
T2 - IEICE TRANSACTIONS on Information
SP - 3005
EP - 3016
AU - Malik Jahan KHAN
AU - Mian Muhammad AWAIS
AU - Shafay SHAMAIL
PY - 2010
DO - 10.1587/transinf.E93.D.3005
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2010
AB - Inspired from natural self-managing behavior of the human body, autonomic systems promise to inject self-managing behavior in software systems. Such behavior enables self-configuration, self-healing, self-optimization and self-protection capabilities in software systems. Self-configuration is required in systems where efficiency is the key issue, such as real time execution environments. To solve self-configuration problems in autonomic systems, the use of various problem-solving techniques has been reported in the literature including case-based reasoning. The case-based reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities. The learning process improves as more experience is added in the case-base in the form of cases. This results in a larger case-base. A larger case-base reduces the efficiency in terms of computational cost. To overcome this efficiency problem, this paper suggests to cluster the case-base, subsequent to find the solution of the reported problem. This approach reduces the search complexity by confining a new case to a relevant cluster in the case-base. Clustering the case-base is a one-time process and does not need to be repeated regularly. The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework. The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA). This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case-base in the proposed framework. The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy, Recall and Precision (ARP) and computational efficiency.
ER -