Virtual Environment Resource Deviation Detection
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Solution Overview
Problem
Managing cloud computing resources is complex due to the large number of instances and interactions within cloud computing data centers, making it difficult to predict performance degradations, which can lead to poor customer experiences and service provider changes.
Innovation Solution
A system that monitors customer usage patterns and resource operational data to detect unhealthy resources by comparing usage patterns to baseline patterns, allowing for automated remediation and resource management decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used to track computing resources, then the system can maintain basic oversight, but it fails to accurately detect performance degradations and unhealthy resources in time
Solution Approach 1:
The patent implements feedback mechanisms by continuously collecting resource usage data from multiple sources (customer usage patterns, operational data, metrics) and feeding this information back into the analysis system. The system compares actual usage against baseline patterns and automatically detects deviations, creating a closed-loop feedback system that improves detection accuracy without requiring overly complex manual monitoring procedures.
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between raw resource data and detection outcomes. This intermediary system aggregates data from multiple sources, normalizes different data formats, and applies baseline comparison algorithms, thereby simplifying the overall system architecture while improving detection reliability through systematic data mediation.
2Measurement precision
If comprehensive resource monitoring is implemented across all computing instances, then detection accuracy improves, but the complexity of managing and analyzing the large volume of data increases significantly
Solution Approach 1:
The patent segments the monitoring system into distinct functional components: data collection modules that gather raw usage data, baseline establishment modules that create reference patterns, comparison modules that detect deviations, and response modules that act on detections. This segmentation allows each component to specialize in specific tasks, improving measurement precision while distributing data management complexity across manageable segments rather than requiring a monolithic complex system.
Solution Approach 2:
The patent applies partial action by focusing monitoring efforts on detecting deviations from baseline patterns rather than attempting to analyze every aspect of resource usage in detail. The system collects comprehensive data but selectively processes only the portions that indicate potential issues, thereby achieving high detection precision without the proportional increase in overall system complexity that would result from exhaustive analysis of all data points.
3Productivity
If manual methods are used to identify and respond to unhealthy resources, then the system can maintain simple architecture, but response time increases and customer experience deteriorates
Solution Approach 1:
The patent implements self-service automation where the monitoring system automatically detects unhealthy resources by comparing usage patterns against baselines, automatically generates notifications, and can trigger remediation actions without human intervention. This self-service capability dramatically improves productivity and response time, while the automation rules and algorithms keep the added complexity manageable by eliminating the need for complex manual coordination procedures.
Solution Approach 2:
The patent applies preliminary action by pre-establishing baseline usage patterns for resources before deviations occur. These baselines are created in advance through data collection and analysis, enabling the system to quickly detect anomalies when they happen. This preliminary preparation work, done before actual monitoring needs arise, improves response productivity while keeping the real-time system relatively simple by relying on pre-computed reference data rather than complex real-time analysis algorithms.
Data Source
AI summary
Systems and methods for detecting deviating resources in a virtual environment are disclosed. In one embodiment, a method comprises monitoring tenant usage of a resource of the virtual environment to determine a tenant usage pattern of the resource. The resource is determined to be deviating based on the tenant usage pattern of the resource.