FailureSim: A System for Predicting Hardware Failures in Cloud Data Centers Using Neural Networks

Abstract

Hardware failures in cloud data centers may cause substantial losses to cloud providers and cloud users. Therefore, the ability to accurately predict when failures occur is of paramount importance. In this paper, we present FailureSim, a simulator based on CloudSim that supports failure prediction. FailureSim obtains performance related information from the cloud and classifies the status of the hardware using a neural network. Performance information is read from hosting hardware and stored in a variable length windowing vector. At specified stages, various aggregation methods are applied to the windowing vector to obtain a single vector that is fed as input to the trained classification algorithm. Using conservative host failure behavior models, FailureSim was able to successfully predict host failure in the cloud with roughly 89% accuracy.