Local ML Model Latency Diagnosis for Real-Time Updates
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Solution Overview
Problem
Communication devices experience performance delays and latency issues after update procedures, making them unreliable during operations.
Innovation Solution
A system and method that employs an ML algorithm to identify and correct latency issues in local ML models by analyzing information parameters, determining latency causes, and performing corrective operations to update the models in real-time without reverting to previous configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If update procedures are executed to modify or replace configuration, then system functionality is improved, but performance delays and latency issues occur
Solution Approach 1:
The system performs preliminary actions by classifying latency issues into categories (data changes, data sizes, model sizes, network latencies) before implementing corrective operations. This preliminary classification enables targeted corrections that prevent performance delays from affecting system reliability during operations.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring local ML models for latency issues and automatically executing corrective operations based on detected problems. This closed-loop feedback ensures that configuration updates maintain system reliability while improving functionality.
2Reliability
If corrective operations are performed to reduce latency, then system reliability is improved, but processor and memory usage increase
Solution Approach 1:
The system applies local quality by targeting specific elements in information parameters (triggers, outputs, data sets) for corrective operations rather than performing comprehensive system-wide corrections. This localized approach reduces processor and memory usage while maintaining reliability improvements.
Solution Approach 2:
The system changes specific parameters in the local ML model configuration to correct latency issues. By modifying only the necessary parameters (data changes, data sizes, model sizes) rather than the entire system, the system achieves reliability improvements with minimal impact on processor and memory resources.
3Productivity
If local ML models are updated in real-time, then productivity is improved, but latency issues may be introduced
Solution Approach 1:
The system performs preliminary classification of latency causes before executing corrective operations during real-time updates. This preliminary action ensures that corrections are applied efficiently without introducing additional latency, maintaining productivity while preventing time loss.
Solution Approach 2:
The system rushes through unnecessary corrective operations by skipping steps that do not address the identified latency cause. This selective correction approach maintains real-time update capability while minimizing time loss from unnecessary processing.
Data Source
AI summary
An apparatus comprises a memory communicatively coupled to a processor. The processor is configured to receive information parameters associated with a machine learning (ML) model of the one or more ML models and execute an ML algorithm to evaluate the information parameters in accordance with one or more latency classification operations. The one or more latency classification operations are configured to determine whether the ML model comprises multiple latency complications. Further, the processor is configured to generate multiple analysis results indicating that the ML model comprises the latency complications in response to evaluating the information parameters, determine a latency cause of the latency complications based on the analysis results, and determine multiple corrective operations configured to correct the latency cause. The processor is configured to update the ML model to comprise the corrective operations and generate a report configured to release an updated version of the ML model.