ML-Based Computing Architecture Recommendation for Distributed Systems
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Solution Overview
Problem
The increasing complexity of distributed software systems across multiple computing nodes, especially with containerization, makes it difficult to define and verify optimal computing architectures, leading to challenges in testing and deployment, including performance faults and resource allocation.
Innovation Solution
A machine learning model is trained using historical testing data from multiple software systems to recommend optimal computing architectures, which can be used during deployment or to dynamically reconfigure existing systems, considering various resources and configurations, including memory, I/O bandwidth, network, and processor resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software systems are distributed across multiple computing nodes with containerization, then system functionality and scalability are improved, but testing complexity and verification difficulty increase
Solution Approach 1:
The patent introduces an intermediary system that automatically generates, executes, and analyzes test cases across distributed computing nodes. This intermediary testing framework mediates between the complex distributed system architecture and the verification process, automatically handling the orchestration of tests across containers and nodes without requiring manual intervention for each test scenario.
Solution Approach 2:
The patent applies preliminary action by performing comprehensive automated testing and verification before system deployment. Test cases are generated and executed in advance to identify potential failures, and the system uses historical test data to predict and prevent failures before they occur in production environments.
2Productivity
If computing architecture is optimized for specific software systems, then system performance is improved, but the difficulty of defining and verifying optimal architecture increases
Solution Approach 1:
The patent implements feedback mechanisms that automatically monitor system performance metrics and compare them against expected benchmarks. The system collects performance data from deployed systems, analyzes it to identify deviations, and uses this feedback to refine architecture recommendations and generate corrective actions, creating a continuous improvement loop.
Solution Approach 2:
The patent replaces manual architecture verification processes with automated machine learning models and performance analysis systems. These automated systems objectively measure and evaluate architecture effectiveness, substituting subjective human judgment with data-driven automated assessment.
3Reliability
If historical testing data is collected and analyzed, then failure prediction accuracy is improved, but data processing and model training time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing historical testing data in advance, creating structured datasets that are ready for rapid analysis. Test results, system configurations, and performance metrics are collected and standardized beforehand, reducing the time required for actual failure prediction when the models are deployed.
Solution Approach 2:
The patent uses copying by creating simplified representations or proxies of complex historical data sets. Instead of processing all raw historical testing data, the system uses sampled subsets, aggregated statistics, or compressed data representations that capture the essential patterns while requiring significantly less processing time.
Data Source
AI summary
Data is received that characterizes a software system. Thereafter, using at least one machine learning model trained using historical testing data from a plurality of training software systems, a recommended computing architecture is generated for the software system. Data can then be provided that characterizes the software system. Related apparatus, systems, techniques and articles are also described.