Description

Disk-current storage systems, to protect our data to security. If the disk is failure Machine learning proved to be a method to find the problem of disk failure prediction. We explore machine learning for accurately predicting imminent disk failures and hence providing proactive fault tolerance for modern storage systems. Current disk failure prediction approaches are mostly offline in this we implement online disk failure prediction and assume that the disk logs required for training learning models are available a priori. we design StreamDFP with three key techniques, namely (i) online labeling, (ii) concept-drift-aware training, and (iii) general prediction, with a primary objective of making StreamDFP support various machine learning algorithms as a general frame-work. Our evaluation shows that StreamDFP improves the prediction accuracy significantly compared to without concept-drift adaptation under various settings, and achieves reasonably high stream processing performance.