Hang State Detection via Heuristic Resource Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to effectively distinguish between a computing system in a hang state and an idle state, leading to incorrect conclusions and potential faulty interventions that affect performance and availability.
Innovation Solution
Employing heuristics to analyze resource consumption levels and system workloads, identifying hang states through detection of no-progress cycles or chains, and distinguishing them from idle states characterized by no resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource consumption monitoring is used to detect system states, then system responsiveness can be improved, but false differentiation between idle and hang states occurs
Solution Approach 1:
The patent segments the detection process into multiple independent analysis components: (1) resource consumption pattern analysis, (2) system call frequency analysis, (3) process state analysis, and (4) temporal pattern analysis. Each segment evaluates a specific aspect of system behavior, and their combined results enable accurate differentiation between idle and hang states, resolving the measurement precision issue while maintaining reliability.
Solution Approach 2:
The patent transforms the detection approach by changing from monitoring a single resource consumption parameter to analyzing multiple dynamic parameters simultaneously: resource consumption levels, system call frequencies, process state transitions, and temporal patterns. This multi-parameter analysis enables precise state differentiation, eliminating false positives while maintaining system responsiveness.
2Device complexity
If simple resource monitoring is implemented, then implementation complexity is reduced, but state detection accuracy deteriorates
Solution Approach 1:
The detection system is segmented into modular components that can be independently implemented and analyzed: resource consumption monitors, system call trackers, process state analyzers, and pattern recognition modules. This segmentation reduces implementation complexity by allowing incremental deployment while maintaining high detection accuracy through the coordinated operation of multiple specialized modules.
Solution Approach 2:
The patent introduces intermediary analysis layers that process raw monitoring data before final state determination. These intermediaries include temporal pattern analyzers and correlation engines that transform simple resource metrics into meaningful state indicators, achieving high detection accuracy without requiring direct complex hardware or system modifications.
3Productivity
If corrective actions are taken based on inaccurate state detection, then system intervention frequency increases, but system performance and availability deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the detection system continuously monitors system state and adjusts its analysis parameters based on observed patterns. The system learns from false positives and negatives, refining its differentiation criteria over time. This feedback loop ensures that corrective actions are taken only when truly necessary, maintaining high system availability while enabling appropriate intervention frequency.
Solution Approach 2:
The system performs preliminary analysis of multiple indicators before triggering corrective actions. By evaluating resource consumption patterns, system call frequencies, and process states in advance, the system confirms the actual system state before initiating interventions. This preliminary verification prevents unnecessary corrective actions, preserving system availability while maintaining appropriate responsiveness to genuine issues.
Data Source
AI summary
A system, method, and computer program product are described for distinguishing between a computing system that is hung in a hang state and systems that are in an idle or otherwise non-hang state which do not need intervention before regaining the ability to adequately process work. According to some approaches, heuristics are employed to perform hang and idle system detection and validation. Data representative of systems resources are analyzed and transformed in order to identify systems that are in a hang state.