New Metrics for Disk Failure Prediction That Go Beyond Prediction Accuracy

Significance of Disk Failure Prediction in Datacenters

Modern datacenters assemble a very large number of disk drives under a single roof. Even if economic and technical factors where to make individual drives more reliable (which is not at all clear, given the commoditization of the technology), their sheer numbers combined with their ever increasing utilization in a well-balanced design makes achieving storage reliability a major challenge. In th...

Bayesian approaches to failure prediction for disk drives

Hard disk drive failures are rare but are often costly. The ability to predict failures is important to consumers, drive manufacturers, and computer system manufacturers alike. In this paper we investigate the abilities of two Bayesian methods to predict disk drive failures based on measurements of drive internal conditions. We first view the problem from an anomaly detection stance. We introdu...

A New Hybrid Prediction Approach For Enhance Prediction Accuracy of Complex Data

Over past few decades a lot of approaches have introduced for data prediction practice and many of researchers are continuously introducing their unique ideas on a daily basis for enhancing the power of prediction system in different areas but each and every approach has its own limitation. One of the most limitations of existing prediction approaches is that they are designed with the strength...

Local Move Prediction in Go

Surprise beyond prediction error

Surprise drives learning. Various neural "prediction error" signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and account...