Thread Dump Profiling for Cloud Service Latency Bottlenecks
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Solution Overview
Problem
Existing automated test systems for cloud-hosted business transaction management and analytics services do not effectively test for performance or efficiency, particularly in large-scale environments with numerous tenants and datasets, leading to suboptimal resource utilization and execution.
Innovation Solution
A system that utilizes thread dumps to derive a relational model from the execution of code, analyzing thread intensity and latency information to profile performance and identify bottlenecks, using a dashboard for real-time diagnostics and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated test systems are used to test code execution, then code functionality can be validated, but performance and efficiency cannot be effectively tested
Solution Approach 1:
The patent introduces thread dumps as an intermediary mechanism to bridge the gap between code execution and performance measurement. Thread dumps capture runtime state information without interfering with normal execution, enabling performance analysis while maintaining reliable code testing.
Solution Approach 2:
The patent replaces traditional mechanical performance testing approaches with a software-based threading model. By using thread dumps to capture execution state and analyzing thread intensity and latency through software processing, the system achieves performance measurement without additional hardware complexity.
2Adaptability or versatility
If cloud services scale to handle more tenants and datasets, then service capacity increases, but resource utilization becomes suboptimal
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring thread dumps and generating performance profiles that identify resource consumption patterns. This feedback enables the system to optimize resource allocation dynamically, matching service capacity with actual resource needs to reduce waste.
Solution Approach 2:
The patent analyzes changes in threading parameters such as thread intensity, latency, and resource consumption across different service scales. By monitoring how these parameters change as services handle more tenants and datasets, the system identifies optimization opportunities to improve resource utilization efficiency.
3Productivity
If thread dumps are collected and analyzed to profile performance, then execution efficiency can be optimized, but system overhead increases
Solution Approach 1:
The patent applies partial action by collecting thread dumps at strategic points rather than continuously monitoring all threads. It focuses analysis on specific threading patterns and performance-critical sections, reducing the overhead of data collection while maintaining effective performance profiling capability.
Solution Approach 2:
The patent extracts key performance information from the complex data captured in thread dumps, separating the essential metrics (thread intensity, latency, resource consumption) from the redundant execution trace data. This extraction process reduces processing overhead while preserving the information needed for optimization.
Data Source
AI summary
A method, system, and device for profiling execution of a set of code is disclosed. The method includes obtaining a plurality of thread dumps, determining a relational model based at least in part on the plurality of thread dumps, and determining, based at least in part on the relational model, latency information pertaining to execution of at least part of the set of code.