Node State Comparison for Anomaly Detection in Data Centers
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Solution Overview
Problem
Current methods for detecting anomalies in large-scale networked systems, such as data centers, are labor-intensive, error-prone, and reactive, often requiring manual inspections that are costly and time-consuming, leading to potential security vulnerabilities and functionality issues due to misconfigurations or external threats.
Innovation Solution
A self-learning system that proactively and automatically detects anomalies within nodes of a distributed cloud-computing infrastructure using a comparison technique, clustering nodes based on similarity of configuration, and identifying potential misconfigurations or security breaches without relying on pre-defined process lists or configuration updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to detect anomalies in nodes, then detection accuracy can be maintained through human expertise, but the process becomes labor-intensive, time-consuming, and costly
Solution Approach 1:
The system enables nodes to self-diagnose by automatically comparing their state information against learned normal patterns. Each node generates its own anomaly detection results without requiring manual inspection, thereby eliminating labor-intensive human review while maintaining detection accuracy through automated machine learning models.
Solution Approach 2:
The patent replaces manual human inspection (mechanical process) with automated computational analysis. Machine learning models process state information from nodes automatically, substituting human expertise with algorithmic detection that operates at scale without time constraints associated with manual review.
2Reliability
If conventional hard-coded process lists are used for detection, then security alerts can be generated for rogue processes, but the maintenance cost becomes extremely high due to frequent configuration updates
Solution Approach 1:
The system transitions from static hard-coded process lists to dynamic machine learning models that continuously adapt to changing system configurations. The models are retrained on new data automatically, enabling the detection system to evolve with the infrastructure without requiring manual updates to configuration lists, thereby reducing maintenance effort while maintaining reliability.
Solution Approach 2:
The system implements continuous feedback loops where detection results and system state changes are fed back into the machine learning models for ongoing refinement. This automatic feedback mechanism allows the system to maintain high detection reliability through continuous learning without requiring manual intervention for configuration updates.
3Measurement precision
If individualized manual inspections are performed on each node, then detailed anomaly detection can be achieved, but the process becomes error-prone and does not guarantee consistent results across the data center
Solution Approach 1:
The patent implements a universal machine learning model that applies the same detection logic across all nodes in the data center. This single standardized system performs detailed anomaly detection on every node consistently, eliminating variability introduced by different human inspectors while maintaining high detection detail through the model's comprehensive analysis capabilities.
4Productivity
If reactive detection methods are used where inspections occur only after issues are reported, then resource utilization is optimized by avoiding unnecessary inspections, but security vulnerabilities and functionality issues persist undetected
Solution Approach 1:
The system performs preliminary continuous monitoring and automated anomaly detection on all nodes before issues manifest as security vulnerabilities or functionality problems. By proactively identifying anomalies through continuous automated analysis, the system prevents problems rather than reacting to them, maintaining both security and efficient resource utilization by only triggering alerts when actual anomalies are detected.
Data Source
AI summary
Methods, systems, and computer storage media for detecting anomalies within nodes of a data center are provided. A self-learning system is employed to proactively and automatically detect the anomalies using one or more locally hosted agents for pulling information that describes states of a plurality of nodes (e.g., computing devices of a cloud-computing infrastructure), respectively, and using at least one early-warning mechanism for implementing a comparison technique. The comparison technique involves individually comparing the state information of the plurality of the nodes against one another and, based upon the comparison, grouping one or more nodes of the plurality of nodes into clusters that exhibit substantially similar state information. Upon identifying the clusters that include low number of nodes grouped therein, with respect to a remainder of the clusters of nodes, the members of the identified clusters are designated as anomalous machines.