Distributed Tracing Optimization via Frequency Analysis
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Solution Overview
Problem
Detailed tracing operations during development and debugging can adversely affect system performance by consuming large amounts of processing, storage, and network bandwidth, leading to performance degradation and resource exhaustion.
Innovation Solution
A distributed tracing system divides trace objectives across multiple application instances, performs cost analysis to identify burdensome objectives, and optimizes data collection by analyzing periodicities, allowing for lightweight, non-intrusive tracing workloads that provide a holistic view of application performance while minimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed tracing operations are performed during development and debugging, then tracing data completeness and analysis accuracy are improved, but system performance deteriorates due to large processing, storage, and network bandwidth consumption
Solution Approach 1:
The patent segments the tracing system into multiple independent trace objectives that can be executed separately. Each trace objective focuses on specific functions, processes, or events, allowing the system to collect comprehensive tracing data without requiring all objectives to run simultaneously, thus reducing resource consumption while maintaining data completeness.
Solution Approach 2:
The patent implements cost analysis to identify and execute only the most critical or burdensome trace objectives based on processing, storage, and network costs. By selecting a subset of objectives that provide the most value, the system achieves adequate tracing data collection without the excessive resource consumption of running all possible trace objectives.
2Measurement precision
If trace objectives are deployed on multiple application instances, then tracing coverage and statistical significance are improved, but system complexity and resource management burden increase
Solution Approach 1:
The patent creates trace objectives that can be universally deployed across multiple application instances with identical or similar configurations. The same trace objective definitions can be applied to different instances, simplifying management while achieving comprehensive coverage through aggregation of results from multiple instances.
Solution Approach 2:
The patent copies trace objective definitions across multiple application instances rather than creating unique objectives for each instance. This copying approach maintains consistency in tracing methodology while enabling statistical analysis across instances, reducing the complexity of managing unique configurations for each instance.
3Loss of energy
If cost analysis is performed to identify burdensome trace objectives, then resource consumption is reduced, but tracing data quality may be compromised
Solution Approach 1:
The patent implements cost analysis that evaluates trace objectives based on processing, storage, and network costs, then uses this feedback to select which objectives to execute. The system continuously monitors resource consumption patterns and adjusts the selection of trace objectives to maintain data quality while optimizing resource usage, ensuring that the most informative objectives are prioritized.
Solution Approach 2:
The patent changes the parameters of trace objectives dynamically based on cost analysis results. When resources are constrained, the system modifies which objectives are active, adjusts sampling rates, or changes data collection frequency, allowing the tracing system to adapt to available resources while maintaining the highest possible data quality within those constraints.
Data Source
AI summary
Tracer objectives in a distributed tracing system may be compared to identify input parameters that may have a high statistical relevancy. An iterative process may traverse multiple input objects by comparing results of multiple tracer objectives and scoring possible input objects as being possibly statistically relevant. With each iteration, statistically irrelevant input objects may be discarded from a tracer objective and other potentially relevant objects may be added. The iterative process may converge on a set of statistically relevant input objects for a given measured value without a priori knowledge of an application being traced.