Service Performance Simulation via Autoencoder Inference
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Solution Overview
Problem
Current methods for simulating service performance in the software development life cycle are inefficient, as they either require recording and replaying actual service traffic or creating a full production environment, which is resource-intensive and not feasible early in the SDLC.
Innovation Solution
A simulation architecture using an autoencoder to encode raw performance metric values into inferred values, representing dominant trends, and a container to generate simulated responses based on these inferred metrics, allowing for early testing without a full production environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual service traffic is recorded and replayed for performance simulation, then measurement precision is improved, but device complexity and resource overhead increase
Solution Approach 1:
The patent creates a simplified copy of service performance characteristics using autoencoders. Instead of replaying actual traffic, the system encodes raw performance metrics into compressed representations and reconstructs them to simulate performance patterns. This copying approach maintains measurement precision while reducing the complexity of setting up full traffic replay infrastructure.
Solution Approach 2:
The patent replaces the mechanical approach of traffic recording and replaying with a machine learning-based system. Autoencoders substitute for traditional traffic generation mechanisms, using learned patterns from historical data to generate realistic performance simulations without requiring actual traffic capture and replay infrastructure.
2Reliability
If a full production environment is created for service testing, then reliability of performance data is improved, but use of energy and resource overhead worsen
Solution Approach 1:
The patent extracts only the essential performance characteristics from the production environment using autoencoders. Instead of replicating the entire production system, the system extracts key performance patterns from historical data and uses them for simulation. This extraction approach maintains data reliability while significantly reducing resource overhead by eliminating the need for full production environment replication.
Solution Approach 2:
The patent creates a virtual copy of production performance patterns through machine learning models. The autoencoder-based system learns and reproduces performance characteristics without requiring actual production systems to be running, enabling reliable performance testing with minimal resource consumption.
3Productivity
If general simulators are used to aggregate empirical performance data, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms raw performance metrics into encoded representations using autoencoders, changing the parameter space to capture essential patterns more effectively. This transformation maintains measurement precision by learning optimal encodings from data, while improving productivity through automated pattern generation that doesn't require manual simulator configuration.
Solution Approach 2:
The patent replaces traditional aggregation-based simulators with machine learning-based pattern generation. Instead of mechanically aggregating empirical data, the system uses autoencoders to learn and generate performance patterns, maintaining precision while improving productivity through automated, data-driven simulation.
Data Source
AI summary
A method may include obtaining, for each period within a time interval, raw performance metric values for a performance metric of a service, encoding the raw performance metric values to generate inferred performance metric values for the performance metric, and selecting a portion of the inferred performance metric values. The portion corresponds to a representative period. The may method further include obtaining a service request for the service, generating a simulated response to the service request, and transmitting the simulated response using the portion of the inferred performance metric values.