Scaled-Down Load Test Models for Virtual Node Simulation
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Solution Overview
Problem
Load testing systems and software services under real-world high loads is challenging due to the difficulty in replicating and cost-effectiveness of simulating high loads, as systems often degrade only under prolonged exposure, making it hard to identify and address inefficiencies.
Innovation Solution
A system utilizing a processor and memory to create a test environment with virtual nodes that apply progressively smaller virtual loads, mimicked by a machine learning algorithm to generate a scaled-down load test model that replicates the behavior of real-world loads, allowing for efficient simulation of high loads without the need for prolonged testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high load testing is performed to identify system degradation points, then system performance understanding is improved, but testing cost and time increase significantly
Solution Approach 1:
The patent creates virtual copies of the production system environment with virtual nodes that replicate the behavior and characteristics of real system nodes. These virtual copies allow load testing to be performed without requiring actual high-load production traffic, significantly reducing testing time while maintaining measurement validity. The virtual system mimics the production system's response to various load conditions, enabling efficient performance evaluation.
Solution Approach 2:
The patent transforms the load testing approach by changing the parameter of load magnitude. Instead of applying full production-level loads that require extended time to manifest degradation, the system uses machine learning to predict degradation points from scaled-down virtual load tests. This parameter transformation allows rapid testing at lower loads while accurately forecasting system behavior at high loads.
2Measurement precision
If real-world high loads are applied to test system degradation, then accurate performance data is obtained, but testing becomes cost prohibitive
Solution Approach 1:
The patent replaces expensive real-world high-load testing with virtual system copies that can be tested at fraction of the cost. The virtual nodes consume minimal computational resources compared to actual production traffic, eliminating the prohibitive costs associated with generating and managing real high-load test scenarios while preserving the ability to detect performance degradation patterns.
Solution Approach 2:
The patent substitutes the mechanical approach of physically applying high loads to the system with a computational approach using machine learning algorithms. Instead of mechanically generating high-volume real traffic to induce degradation, the system uses algorithms to analyze virtual system responses and predict degradation points, replacing costly physical testing with efficient computational analysis.
3Productivity
If virtual nodes with scaled-down loads are used for testing, then testing cost and time are reduced, but system accuracy may be compromised
Solution Approach 1:
The patent incorporates feedback loops where the machine learning algorithm continuously learns from the virtual system's responses to virtual loads. The system monitors performance metrics from virtual nodes under various scaled-down load conditions, uses this feedback to refine its degradation prediction model, and iteratively improves accuracy. This feedback mechanism ensures that despite using reduced loads, the system achieves high measurement precision in predicting real-world degradation points.
Solution Approach 2:
The patent performs preliminary testing with scaled-down virtual loads to gather performance data before actual high-load conditions are applied. The machine learning algorithm uses this preliminary data from virtual tests to predict degradation points, enabling accurate performance assessment without requiring expensive and time-consuming real high-load testing. The preliminary virtual testing establishes a foundation for accurate predictions.
Data Source
AI summary
Methods, systems, apparatus, and program products that can generate scaled-down load test models for testing real-world loads are disclosed herein. One method includes providing a test environment of a system including multiple nodes. The test environment includes virtual nodes corresponding to the system nodes and each virtual node functions under a virtual load similar to each corresponding node functioning under a real-world load. The method further includes utilizing a machine learning algorithm to repeatedly apply at least one virtual load to the virtual node(s) in the test environment until a scaled-down load test model mimicking the system under a pre-defined real-world load is generated. Here, the virtual load(s) applied to the virtual node(s) is/are comparatively smaller relative to each of corresponding real-world loads for the node(s) defining the pre-defined real-world load. Systems, apparatus, and program products that include and/or perform the methods are also disclosed herein.