A threshold sensitive failure prediction method using support vector machine
Ahmad Fadaei Tehrani, Faramarz Safi-Esfahani · Multiagent and Grid Systems · 2017
One of the current discussions concerning cloud computing environments involves the issue of failure prediction that influences the delivery of on-demand services through the Internet. Proactive failure prediction techniques play an important role in reducing undesirable consequents produced by fai lures within high performance systems. Accordingly, this study aims at proposing a threshold sensitive by using support vector machine to create an efficient mechanism for predicting failure within cloud environments. The new approach can operationally avoid system failures for each host based on log file which include features such as CPU utilization, RAM, and bandwidth, etc. In comparison to the base research, the findings demonstrated that the presented method could better reduce the percent of migrations about 76.19% proactively when the failure threshold level was 70%.