Latency Prediction via Virtual Environment Replication
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Solution Overview
Problem
Existing software testing environments often fail to accurately predict the performance of updated code in live environments due to hardware and software module differences, limiting the applicability of test results.
Innovation Solution
A system utilizing an augmented machine learning model to predict latency changes by obtaining parameter sets from multiple environments, determining deviations, and classifying latencies to forecast performance in production environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code is tested in a special test environment, then testing can be performed with controlled conditions, but the test results have limited applicability to the live environment due to hardware and software module differences
Solution Approach 1:
The patent creates virtual copies of the live environment's hardware and software modules in the test environment. By replicating the exact configuration, hardware specifications, and software module versions of the live environment, the test environment becomes a faithful representation that allows reliable prediction of code behavior in production without requiring actual live environment testing.
Solution Approach 2:
The patent dynamically adjusts environment parameters such as hardware specifications, software module versions, and configuration settings to match the live environment. By changing these parameters to reflect the actual production conditions, the test environment transitions from a generic controlled setting to a representative replica that accurately predicts live performance.
2Ease of manufacture
If test environment hardware differs from live environment hardware, then testing can be performed with available resources, but performance prediction accuracy decreases
Solution Approach 1:
Instead of accepting hardware differences, the patent systematically copies the live environment's hardware specifications, processor types, memory configurations, and storage systems into the test environment. This creates a hardware-identical replica that enables both testing feasibility and accurate performance prediction simultaneously.
Solution Approach 2:
The patent introduces an environment abstraction layer that mediates between the test hardware and the code being tested. This intermediary layer virtualizes the hardware interface to match the live environment's characteristics, allowing tests to run on available test hardware while maintaining accurate performance predictions through virtual hardware representation.
3Reliability
If code interacts with other software modules in the live environment, then realistic testing scenarios can be created, but test environment limitations prevent accurate behavior prediction
Solution Approach 1:
The patent creates a universal test environment framework that can accommodate multiple software modules and their interactions in a single standardized configuration. This multi-functional environment replicates the live environment's software ecosystem, allowing comprehensive testing of code interactions without proportionally increasing configuration complexity through automation and modular design.
Solution Approach 2:
The patent segments the complex live environment into discrete, manageable software module units that can be individually replicated and configured in the test environment. By breaking down the ecosystem into modular components with standardized interfaces, the system can accurately represent module interactions while simplifying the overall configuration and management process.
Data Source
AI summary
The present disclosure relates to system and methods for predicting performance caused by software code changes. For this purpose, an augmented machine learning model predicts a latency of software module with updated code executed in a production environment. In some aspects, the latency is predicted based on a change of deviation that is determined by comparing the latency of the software module with updated code and the latency of the software module without updated code, whereas the software modules are executed in environments different from the production environment.