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- 저자 : ( Haoqiu Huang ) (검색결과 3 건)
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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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( Lanlan Rui ),( Yao Zhang ),( Haoqiu Huang ),( Xuesong Qiu ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.1
Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.
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Hao Qiu,Jinshan Huang,Zhupeng Zheng 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.79 No.6
It is necessary to identify structures with unknown mass and unknown seismic excitations simultaneously, but very limited methods have been proposed. In this paper, an algorithm is proposed to identify structural element mass, stiffness, and seismic excitations using only partial absolute structural responses of chain-like systems. In the first stage, the identification of structural element stiffness-mass coupled coefficients and unknown seismic excitations is conducted based on the generalized extended Kalman filter with unknown input (GEKF-UI) developed by the authors. In the second stage, these coupled coefficients are decoupled by cluster analysis and the least squares estimation (LSE) to obtain structural element stiffness and mass changes. The effectiveness of the proposed algorithm is numerically investigated using a four-story frame structure under three scenarios of changed conditions. Moreover, experimental validation by the shaking table test of a shear structure under two scenarios is also performed to identify structures and seismic excitations simultaneously.
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