Predicting the System Failures using Machine Learning Algorithms
Creators
- 1. Assistant Professor, Department of CSE East Point College of Engineering And Technology, Bangalore,
Description
Quick recuperation stays one of the key difficulties to architects and administrators of vast organized framework. Before recuperation can happen, notwithstanding, one should first identify and analyze the disappointment, so without arrange disappointment expectation it is hard to recoup the failure. Failure in any piece of the system result in slow down the system. Failure Prediction helps in early detection of failure in the system, so that we can avoid the damage caused by the failure in that particular system. By predicting the failure we can avoid loss of data, application services. By prediction we can also decide if the framework will breakdown as indicated by the examination on the assessment of authentic investigation and current conduct of the framework.
Files
Paper 4.pdf
Files
(176.6 kB)
Name | Size | Download all |
---|---|---|
md5:72511913982fefe4bf8e432d5fefaf7c
|
176.6 kB | Preview Download |
Additional details
References
- [1] Zhiqiangcai, Weitaosi, Shubinsi and Shudong, Sun. Modeling of failure prediction Bayesian network with divide-and-conquer principle, North western polytechnic university, China 2014.
- [2] Sahana, D. S., and L. Girish. "Automatic drug reaction detection using sentimental analysis." International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 (2015).
- [3] Mike Chen,AliceX.Zheng,Jim Lloyd,MichaelI.Jordan,Eric Brewer University of California at Berkeleyy and eBay Inc. Failure diagnosis using decision trees,2015.
- [4] Jehad Ali, Rehanullah khan, Nasir Ahmad, Imran maqsood computer science engineering, UET Peshwar, Pakistan. Random forest and decision trees, 2012.
- [5] YulingBai , Yunhua Li School of Automation Science and Electrical Engineering Beihang University Beijing, China, Yongmei Liu , Zhao Ma Power Distribution Research Department China Electric Power Research Institute Beijing, China. Short-term Prediction of Distribution Network Faults Based on Support Vector Machine, 2017.
- [6] L, G. (2019). "Anomaly Detection in NFV Using Tree-Based unsupervised Learning Method". International Journal of Science, Technology, Engineering and Management - A VTU Publication, 1(2), Retrieved from http://ijesm.vtu.ac.in/index.php/IJESM/article/view/232
- [7] Rashmi, T. V., and Keshava Prasanna. "Load Balancing As A Service In Openstack-Liberty." International Journal of Scientific & Technology Research 4.8 (2015): 70-73.
- [8] ZHONG, Weili GUO, Zhenhua WANG Study on network failure prediction based on alarm logs, 2016.
- [9] YulingBai, Yunhua Li, Beijing, china, Yongmei Liu, Zhao Ma Short-term prediction of distribution network faults based on support vector machine, 2017.