TY - GEN
T1 - Hybrid prediction model for improving reliability in self-healing system
AU - Yoo, Giljong
AU - Park, Jeongmin
AU - Lee, Eunseok
PY - 2006
Y1 - 2006
N2 - In ubiquitous environments, which involve an even greater number of computing devices, with more informal modes of operation, this type of problem will have rather serious consequences. In order to solve these problems when they arise, effective reliable systems are required. Also, system management is changing from a conventional central administration, to autonomic computing. However, most existing research focuses on healing after a problem has already occurred. In order to solve this problem, a prediction model is required to recognize operating environments and predict error occurrence. In this paper, a hybrid prediction model through four algorithms supporting self-healing in autonomic computing, is proposed. This prediction model adopts a selective healing model, according to system situations for self-diagnosing and prediction of problems using four algorithms. In this paper, a hybrid prediction model is adopted to evaluate the proposed model in a self-healing system. In addition, prediction is compared with existing research and the effectiveness is demonstrated by experiment.
AB - In ubiquitous environments, which involve an even greater number of computing devices, with more informal modes of operation, this type of problem will have rather serious consequences. In order to solve these problems when they arise, effective reliable systems are required. Also, system management is changing from a conventional central administration, to autonomic computing. However, most existing research focuses on healing after a problem has already occurred. In order to solve this problem, a prediction model is required to recognize operating environments and predict error occurrence. In this paper, a hybrid prediction model through four algorithms supporting self-healing in autonomic computing, is proposed. This prediction model adopts a selective healing model, according to system situations for self-diagnosing and prediction of problems using four algorithms. In this paper, a hybrid prediction model is adopted to evaluate the proposed model in a self-healing system. In addition, prediction is compared with existing research and the effectiveness is demonstrated by experiment.
KW - Autonomic computing
KW - Bayesian network
KW - Fuzzy logic
KW - Fuzzy neural network
KW - ID3 algorithm
KW - Prediction model
KW - Reliable system
KW - Self-healing
KW - Ubiquitous computing
UR - https://www.scopus.com/pages/publications/34547284429
U2 - 10.1109/SERA.2006.40
DO - 10.1109/SERA.2006.40
M3 - Conference contribution
AN - SCOPUS:34547284429
SN - 076952656X
SN - 9780769526560
T3 - Proceedings - Fourth International Conference on Software Engineering Research, Management and Applications, SERA 2006
SP - 108
EP - 115
BT - Proceedings - Fourth International Conference on Software Engineering Research, Management and Applications, SERA 2006
T2 - 4th International Conference on Software Engineering Research, Management and Applications, SERA 2006
Y2 - 9 August 2006 through 11 August 2006
ER -