Networked Storage Resource Anomaly Detection
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Solution Overview
Problem
Networked storage environments face challenges in efficiently managing resources and detecting anomalies in application performance across distributed systems, leading to suboptimal infrastructure utilization and potential bottlenecks.
Innovation Solution
A system and method for monitoring and managing resources in networked storage environments, which involves collecting performance data from various resources, calculating anomaly scores, and providing insights for users to identify and correct resource behavior impacting overall application performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource management methods are used in networked storage environments, then infrastructure components can operate, but resource utilization is suboptimal and anomalies are not detected efficiently
Solution Approach 1:
The system performs preliminary actions by continuously collecting performance data from resources and calculating anomaly scores before actual performance degradation occurs. This proactive approach enables early detection of potential issues and allows for preventive maintenance, improving both resource utilization efficiency and anomaly detection capability simultaneously.
Solution Approach 2:
The system implements feedback mechanisms by monitoring resource performance metrics, comparing them against baseline patterns, and generating anomaly scores that feed back into the resource management process. This closed-loop feedback enables continuous optimization of resource allocation and immediate response to performance deviations, resolving the contradiction between efficient utilization and reliable detection.
2Reliability
If comprehensive performance monitoring is implemented across all resources, then anomaly detection improves, but system complexity and computational overhead increase
Solution Approach 1:
The system extracts only the essential performance metrics and anomaly indicators from the complex set of available data, focusing on key parameters that most significantly impact resource performance. By extracting and monitoring only these critical elements rather than all possible metrics, the system achieves high anomaly detection accuracy while maintaining manageable complexity.
Solution Approach 2:
The system changes parameters by transforming raw performance data into normalized anomaly scores through mathematical transformations and statistical analysis. This parameter transformation simplifies the monitoring task by converting multiple complex metrics into a single interpretable anomaly score, reducing system complexity while preserving detection accuracy.
3Measurement precision
If detailed performance data collection is performed for all resources, then insight accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system applies partial action by collecting and processing only the most critical performance data elements needed for anomaly detection, rather than analyzing every available metric in detail. This selective approach maintains high measurement precision for key indicators while significantly reducing overall data processing time and computational overhead.
Solution Approach 2:
The system performs preliminary data processing by pre-calculating baseline performance patterns and anomaly thresholds before actual monitoring begins. This preliminary preparation enables rapid real-time anomaly score calculation without requiring extensive processing of raw data during operational monitoring, thus maintaining accuracy while minimizing processing time.
Data Source
AI summary
Methods and systems for a networked storage environment are provided. An application executed by a computing device using a plurality of resources in a networked storage environment for storing and retrieving application data is identified. Performance data of the plurality of resources is collected and historical performance data is retrieved. The collected and historical performance data for the plurality of resources is used to determine an overall anomaly score for the application indicating behavior of the application over time and individual anomaly scores for each resource with an indicator highlighting behavior of a resource that impacts the overall anomaly score for the application.