Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM

TL;DR

This paper introduces a comprehensive Fault Detection Engine within an intelligent predictive analytics platform for data center infrastructure management, utilizing probabilistic models and machine learning to predict failures, identify root causes, and cluster devices for scalable real-time fault detection.

Contribution

The paper presents a novel architecture integrating failure prediction, root cause analysis, and community detection modules for fault diagnosis in large device networks.

Findings

Engine successfully predicts failure severity and survival probability.

Root cause analysis accurately identifies potential faulty devices.

Clustering reduces search space for fault localization.

Abstract

With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc. where downtime minimization is one of the primary objectives. This paper proposes a novel, complete architecture of an intelligent predictive analytics platform, Fault Engine, for huge device network connected with electrical/information flow. Three unique modules, here proposed, seamlessly integrate with available technology stack of data handling and connect with middleware to produce online intelligent prediction in critical failure scenarios. The Markov Failure module predicts the severity of a failure along with survival probability of a device at any given instances. The Root Cause Analysis model indicates probable devices as potential root cause employing Bayesian probability assignment and topological sort. Finally, a community detection algorithm produces correlated clusters of device in terms of failure probability which will further narrow down the search space of finding route cause. The whole Engine has been tested with different size of network with simulated failure environments and shows its potential to be scalable in real-time implementation.