Garbage Collection Tracing with Dynamic Focus on Performance Bottlenecks
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Solution Overview
Problem
Existing garbage collection systems face challenges in efficiently identifying the causes of performance problems without altering program behavior and consuming excessive computational resources.
Innovation Solution
Implementing lightweight monitoring to detect performance problem signals and conduct constrained tracing, collecting specified trace data until a stop trigger occurs, using a designation data structure that specifies a performance problem signal, trace data category, and stop trigger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive trace data collection is performed to diagnose performance problems, then diagnostic accuracy is improved, but computational resource usage increases and program performance deteriorates
Solution Approach 1:
The patent segments trace data collection into multiple categories (e.g., garbage collection events, allocation events, finalization events) and selectively collects data from specific categories based on the detected performance problem type. This allows the system to gather sufficient diagnostic information without collecting all possible trace data, thereby reducing computational overhead while maintaining diagnostic accuracy.
Solution Approach 2:
The patent applies local quality by enabling trace data collection with different parameters and granularities for different problem scenarios. Instead of uniform comprehensive collection, the system adjusts the depth and scope of tracing locally based on the specific performance issue detected, optimizing resource usage while preserving necessary diagnostic information.
2Measurement precision
If extensive trace data is collected to diagnose performance problems, then diagnostic accuracy is improved, but the risk of obscuring or compounding problem causes increases
Solution Approach 1:
The patent implements partial action by collecting trace data selectively based on the detected performance problem type. Rather than continuously collecting all trace data which would excessively impact program performance, the system activates specific tracing categories only when relevant to the detected issue, thereby avoiding the harmful effects of excessive tracing while maintaining sufficient diagnostic capability.
Solution Approach 2:
The system employs periodic action by monitoring performance metrics continuously but activating detailed trace data collection only periodically when performance problems are detected. This approach allows the system to maintain normal operation during healthy states and switch to diagnostic mode only when necessary, reducing the overall impact on program performance while ensuring accurate problem diagnosis.
3Loss of energy
If lightweight monitoring is used to detect performance problems, then resource overhead is reduced, but the ability to gather sufficient trace data for accurate diagnosis is limited
Solution Approach 1:
The patent applies preliminary action by implementing lightweight monitoring that continuously detects performance problem indicators before detailed trace data collection is activated. This preliminary detection phase uses minimal resources to identify when a performance issue exists, and only then does the system activate more resource-intensive trace collection, ensuring that sufficient diagnostic data is gathered while minimizing overall resource overhead.
Solution Approach 2:
The system uses feedback mechanisms where lightweight monitoring continuously provides performance metrics that inform the activation and configuration of trace data collection. Based on the feedback from monitoring data, the system dynamically adjusts the scope and intensity of tracing activities, ensuring that sufficient trace data is collected for accurate diagnosis while maintaining efficient resource utilization through adaptive control.
Data Source
AI summary
Some embodiments perform lightweight monitoring for garbage collection (GC) flow events, then perform focused tracing after detecting a performance problem signal. The tracing is focused by constraints which are specified in a designation data structure, including a performance problem signal definition, a corresponding trace data category and a corresponding tracing stop trigger. Tracing is done only in the specified category and only for the specified time period, to reduce or avoid collection of irrelevant trace data and to reduce or avoid changes in program behavior caused by the tracing itself. Some designation data structures also specify a corresponding trace data analysis. In operation, some embodiments dynamically re-focus tracing on an offshoot trace in response to a trace data analysis result obtained while the program is still executing.