閱讀全文 | |
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 Lee、Wei-Guang Teng、Mu-Kai Huang、Chih-Pin Freg、Ting-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-based、CWMP、Self-learning、Predictor、Crash 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 |
|
QR Code | |
該期刊 上一篇
| Text Coverless Information Hiding Method Based on Hybrid Tags |
該期刊 下一篇
| MAKA: Provably Secure Multi-factor Authenticated Key Agreement Protocol |