Autonomous Data Center Self-Healing Modules
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Solution Overview
Problem
Traditional data center management systems are inadequate in coping with the growing complexity and increased compute density of modern data centers, leading to inefficiencies in operational management and a potential skill shortage in IT industries.
Innovation Solution
A computer-implemented method and system for autonomous computing that processes historical data to analyze past performance, collects data from connected devices, synchronizes it, detects alert conditions, and triggers corrective actions through virtual self-healing modules, enabling self-healing and dynamic resource allocation within data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data center management systems are used, then operational management can be performed, but the systems are inadequate in coping with growing complexity and increased compute density
Solution Approach 1:
The patent implements autonomous self-healing modules that automatically detect, diagnose, and resolve issues without human intervention. The system monitors its own health metrics, identifies anomalies, and executes corrective actions autonomously, allowing the data center to adapt to complexity through self-service capabilities rather than requiring increasingly complex external management systems
Solution Approach 2:
The system performs preliminary actions by continuously analyzing historical and real-time data to predict potential failures before they occur. Machine learning models process patterns from historical data to anticipate issues, enabling proactive maintenance and resource allocation that prevents problems rather than reacting to them, thus adapting to complexity through advance preparation
2Reliability
If manual monitoring and management is used, then operational control is maintained, but skill shortage in IT industries creates vulnerability
Solution Approach 1:
The autonomous data center management system performs self-monitoring, self-diagnosis, and self-healing operations without requiring skilled human operators. The system automatically detects failures, identifies root causes, and executes corrective actions, maintaining operational reliability while eliminating dependence on scarce skilled IT personnel
Solution Approach 2:
The patent replaces manual mechanical monitoring and troubleshooting processes with automated electronic systems. Machine learning algorithms and automated diagnostic tools substitute for human expertise, transforming the operational model from skill-dependent manual intervention to skill-independent automated management, thereby improving reliability while easing operational requirements
3Productivity
If autonomous self-healing modules are implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The autonomous management system is segmented into specialized functional modules: data collection modules, historical data analysis modules, anomaly detection modules, diagnosis modules, and self-healing modules. Each module performs a specific function independently, allowing the system to achieve high operational efficiency through specialized automation while managing complexity through modular functional decomposition
Solution Approach 2:
The system implements universal machine learning models and automated diagnostic algorithms that can handle multiple types of failures and operational issues across different data center components. This multi-functionality allows a single autonomous system to manage diverse operational challenges efficiently without requiring separate specialized systems for each function, thereby improving productivity while controlling overall system complexity
Data Source
AI summary
Methods and systems for autonomous computing comprising processing historical data to analyze a past performance, collecting data from a plurality of connected devices over a network, synchronizing the collected data from the plurality of connected devices with the processed historical data. Based on the synchronized data, methods and systems disclosed include detecting an alert (error/fault) condition in one or more of the plurality of connected devices, based on the detected alert condition, triggering the delivery of the detected alert condition to an automated network operations center (NOC), and matching the determined alert condition to a historical alert condition by the network operations center. Based on the matching, methods and systems include determining a corrective action, and based on the determined corrective action, assigning a virtual self-healing module from a plurality of virtual self-healing modules. Finally, a trigger to performance of the determined corrective action by the assigned virtual self-healing module is initiated.