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RISS 인기검색어
- 검색키워드
- 저자 : ( Luoming Meng ) (검색결과 2 건)
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Clustering and Recommendation for Semantic Web Service in Time Series
( Yu Lei ),( Wang Zhili ),( Meng Luoming ),( Qiu Xuesong ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.8
Promoted by cloud technology and new websites, plenty and variety of Web services are emerging in the Internet. Meanwhile some Web services become outdated even obsolete due to new versions, and a normal phenomenon is that some services work well only with other services of older versions. These laggard or improper services are lowering the performance of the composite service they involved in. In addition, using current technology to identify proper semantic services for a composite service is time-consuming and inaccurate. Thus, we proposed a clustering method and a recommendation method to deal with these problems. Clustering technology is used to classify semantic services according to their topics, functionality and other aspects from plenty of services. Recommendation technology is used to predict the possible preference of a composite service, and recommend possible component services to the composite service according to the history information of invocations and similar composite services. The experiments show that our clustering method with the help of Ontology and TF/IDF technology is more accurate than others, and our recommendation method has less average error than others in the series of missing rate.
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EHMM-CT: An Online Method for Failure Prediction in Cloud Computing Systems
( Weiwei Zheng ),( Zhili Wang ),( Haoqiu Huang ),( Luoming Meng ),( Xuesong Qiu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.9
The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A `failure prediction` approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between `failure prediction` performance and computing overhead.
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