Cloud Resource Leak Detection via Usage Pattern Analysis
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Solution Overview
Problem
Cloud computing architectures face performance disruptions due to disproportionate and unmanaged increases in computing resource usage, often caused by software defects leading to resource leakage, which conventional threshold-based systems fail to accurately detect across large scales.
Innovation Solution
A system and process that monitor and analyze computing resource usage patterns across millions of devices, using reference data to identify deviations and detect resource leaks by evaluating usage over time, providing user interfaces for leak identification and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional threshold-based monitoring systems are used to detect resource leaks, then the system complexity is low, but the detection precision and accuracy are insufficient for large-scale cloud environments
Solution Approach 1:
The patent segments the cloud computing environment into multiple hierarchical levels (physical computing devices, virtual machines, containers, software components) and applies monitoring at each level. This segmentation allows precise detection of resource leaks at specific components while managing complexity through structured organization of monitoring data and processes.
Solution Approach 2:
The patent introduces intermediary monitoring agents and software components that act as mediators between the physical computing devices and the central monitoring system. These intermediaries collect, process, and transmit resource usage data, enabling accurate leak detection without requiring direct complex interactions between all system components.
2Reliability
If comprehensive monitoring of all computing resources is implemented, then the detection capability improves, but the computational overhead and processing time increase
Solution Approach 1:
The patent implements preliminary action by establishing baseline resource usage patterns and thresholds before monitoring begins. The system pre-configures monitoring parameters, defines normal vs. abnormal usage patterns, and sets up automated response protocols. This allows the system to quickly identify deviations from normal operation without extensive real-time analysis, reducing processing time while maintaining high reliability.
3Productivity
If manual analysis of resource usage patterns is performed, then the false positive rate is low, but the productivity and response speed are insufficient for large-scale systems
Solution Approach 1:
The patent implements feedback mechanisms where the monitoring system continuously analyzes resource usage data, compares it against baseline patterns, and automatically adjusts monitoring thresholds and parameters. The system provides feedback loops that learn from historical data and improve detection accuracy over time, enabling both high productivity through automation and high precision through adaptive learning.
Solution Approach 2:
The monitoring system performs self-service by automatically detecting, analyzing, and responding to resource leaks without requiring manual intervention. The system autonomously identifies abnormal patterns, determines the source of leaks, and can trigger automated corrective actions, enabling rapid response in large-scale cloud environments while maintaining high accuracy through sophisticated algorithms.
Data Source
AI summary
Techniques and systems for detecting leakage of computing resources in cloud computing architectures are described. In some implementations, first data may be obtained that indicates usage of a computing resource, such as non-volatile memory, volatile memory, processor cycles, or network resources, by a group of computing devices included in a cloud computing architecture. The first data may be used to determine reference data that may include a distribution of values of usage of the computing resource by the group of computing devices. Second data may also be collected that indicates usage of the computing resource by the group of computing devices during a subsequent time frame. The second data may be evaluated against the reference data to determine whether one or more conditions indicating a leak of the computing resource are satisfied.