Distributed Tracing Data Characteristic Prediction Model
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Solution Overview
Problem
Data handling systems in data centers face inefficiencies in making decisions about data handling operations due to the lack of explicit application-level information, which is often unavailable, leading to suboptimal performance and robustness issues.
Innovation Solution
A computer-implemented method and system that utilizes a data characteristic prediction model trained on distributed tracing data to predict characteristics such as file size, lifetime, and access time, allowing for informed handling decisions without requiring explicit application-level information, by leveraging trace logs generated from distributed tracing protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit application-level information is used for data handling decisions, then handling accuracy is improved, but information availability deteriorates (since this information is often unavailable)
Solution Approach 1:
The patent introduces distributed tracing data as an intermediary to bridge the gap between unavailable application-level information and needed data handling decisions. The trace log serves as a mediator that captures service interaction data without requiring direct access to application-level metadata, enabling accurate predictions through this intermediate data source
Solution Approach 2:
The patent replaces manual configuration mechanisms with automated machine learning models. Instead of manually configuring application hints, the system uses ML models that automatically learn from trace log data to make data handling decisions, substituting the mechanical process of manual configuration with an automated intelligent system
2Adaptability or versatility
If manual configuration of application hints is used, then handling decisions are customizable, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically configure data handling decisions without manual intervention. The machine learning models autonomously learn from trace log data and make adaptive handling decisions, allowing the system to serve itself rather than requiring manual configuration by operators
Solution Approach 2:
The patent changes the approach from static manual configuration to dynamic automated learning. The system transitions from fixed configuration parameters to adaptive model predictions that automatically adjust based on learned patterns from trace data, enabling flexibility without manual complexity
3Loss of information
If distributed tracing data is used for predictions, then information availability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex trace log data into structured representations that can be processed by machine learning models. By breaking down the trace data into manageable components and features, the system reduces processing complexity while maintaining the richness of the distributed tracing information
Solution Approach 2:
The patent substitutes manual data processing with automated machine learning inference. Instead of manually analyzing trace logs, the system uses pre-trained ML models that automatically process trace data and generate predictions, replacing complex manual analysis with automated computational processes
Data Source
AI summary
A computer-implemented method for using machine learning to handle data in a computing system with improved efficiency can include obtaining a handling request associated with a data item, the handling request instructing the computing system to perform a handling operation with the data item, obtaining a trace log comprising one or more distributed trace items, the one or more distributed trace items including data from each of one or more services interacting with the data item, providing the trace log to a data characteristic prediction model including one or more machine-learned models, receiving, in response to providing the trace log to the data characteristic prediction model, one or more data characteristic predictions associated with the data item, and selecting a handling operation of a plurality of candidate handling operations based at least in part on the one or more data characteristic predictions.