Exploiting a Self-learning Predictor for Session-based Remote Management Systems in a Large-scale Environment,ERICDATA高等教育知識庫
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篇名
Exploiting a Self-learning Predictor for Session-based Remote Management Systems in a Large-scale Environment
並列篇名
Exploiting a Self-learning Predictor for Session-based Remote Management Systems in a Large-scale Environment
作者 Kuen-Min LeeWei-Guang TengMu-Kai HuangChih-Pin FregTing-Wei Hou
英文摘要
Session-based remote management systems, e.g., customer premises equipment (CPE) WAN management protocol (CWMP), have predictable task counts in a session and each CPE only accesses its own data. When the systems are used in large-scale environments, a static load balancing (LB) policy can be applied with fewer session migrations. Nevertheless, unexpected crash events, e.g., software bugs or improper management, would cause the LB policy to be reassigned so as to degrade the system performance. A self-learning predictor (SLP) is thus proposed in this work to predict unexpected crash events and to achieve a better system performance in terms of resource usage and throughput. Specifically, the SLP records and monitors all crash patterns to evaluate the system stability. Moreover, the relation flags and probabilities of all crash patterns are dynamically updated for quick comparisons. If the SLP finds the current pattern is similar to a crash pattern, a migration request is raised to the load balancer to prevent performance degradation caused by the incoming crash. The simulation results indicate that a better system performance is obtained in a large-scale environment with the proposed SLP, especially as the number of servers in each cluster node increases.
起訖頁 657-668
關鍵詞 Session-basedCWMPSelf-learningPredictorCrash pattern
刊名 網際網路技術學刊  
期數 201805 (19:3期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642018051903004  複製DOI
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