AI Server Issue Detection Using Clustering and Knowledge Bases
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Solution Overview
Problem
Conventional server management techniques fail to identify root causes of software version-related issues, leading to significant instances of false negatives and waste of management resources in data center environments.
Innovation Solution
The use of artificial intelligence techniques, including clustering algorithms and deep learning models, to determine server issues related to software versions, label affected servers, and generate knowledge bases for automated actions, thereby overcoming false negatives and resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional issue management techniques are used to identify server issues, then the system is simple and easy to operate, but the accuracy of identifying root causes is low leading to significant false negatives
Solution Approach 1:
The patent replaces conventional mechanical/manual issue management techniques with artificial intelligence and machine learning systems. The AI model automatically analyzes server data, logs, and metrics to identify root causes of issues, substituting human expert analysis with automated intelligent systems that achieve higher accuracy in identifying software version-related problems without manual intervention
Solution Approach 2:
The patent introduces an intermediary AI analysis layer between raw server data and issue identification. This intermediary system processes and interprets complex server metrics, logs, and configuration data, acting as a mediator that transforms raw data into actionable insights about root causes, thereby improving detection accuracy without requiring direct human analysis of complex data sets
2Productivity
If conventional manual techniques are used to resolve server issues, then automation level is low, but management resources are wasted due to inability to identify root causes
Solution Approach 1:
The patent implements self-service capabilities where the AI system automatically monitors server metrics, identifies issues, determines root causes, and executes remediation actions without human intervention. The system serves itself by autonomously managing the entire issue resolution lifecycle from detection to resolution, eliminating the need for manual resource allocation and reducing management overhead
Solution Approach 2:
The patent performs preliminary actions by proactively monitoring server health metrics and identifying potential issues before they manifest as full-blown problems. The AI system continuously analyzes data patterns and predicts potential failures, allowing preemptive remediation actions to be taken before issues impact service, thereby improving productivity and preventing resource waste on reactive firefighting
3Reliability
If AI techniques are deployed to identify server issues accurately, then false negatives are reduced, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the complex AI system into distinct functional modules: data collection components, preprocessing pipelines, AI model inference engines, and action execution modules. Each module handles a specific aspect of the issue identification process, making the overall system more manageable and easier to implement by breaking down the monolithic AI system into smaller, independently deployable units with clear interfaces
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for determining server issues related to software versions using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more server issues, among multiple reported server issues, as being related to one or more software version states; labeling, using at least one clustering algorithm, each of at least a subset of the servers as being associated with at least one of the determined server issues; generating, by processing data pertaining to at least a portion of the labeled servers using artificial intelligence techniques, at least one knowledge base identifying automated actions to be carried out in response to observed data related to the one or more server issues; monitoring metrics across at least a portion of the one or more servers; and performing one or more automated actions based on the monitoring and the at least one knowledge base.