Virtualized Test Controller for Cloud Workload Modeling
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Solution Overview
Problem
Conventional stress testing of cloud computing infrastructures relies on limited benchmarks, making it difficult to accurately model actual load patterns, leading to potential errors and inefficiencies in resource allocation and performance evaluation.
Innovation Solution
A virtualized-aware automated testing system (VATS) that includes a test controller and method for automated test execution in shared virtualized resource pools, allowing for the orchestration of test provisioning, deployment, execution, and resource management, enabling the evaluation of multiple workloads and resource utilization across cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stress testing uses pre-existing benchmarks with small subset of business objects, then testing can be performed with existing tools, but the synthetic workload cannot accurately model actual load patterns
Solution Approach 1:
The patent creates virtual copies of enterprise applications, business objects, and workload patterns that can be deployed in the cloud environment for testing. These virtual copies accurately replicate the characteristics of actual enterprise systems without requiring physical duplication, enabling realistic workload modeling through virtualization and automation.
Solution Approach 2:
The system dynamically adjusts workload parameters such as transaction volumes, user concurrency, and business process sequences to match actual enterprise patterns. By parameterizing the benchmark configurations, the system can transform generic testing frameworks into customized workload models that accurately reflect specific enterprise requirements.
2Measurement precision
If customized benchmark is created to accurately represent given enterprise, then workload modeling accuracy improves, but development time and cost increase significantly
Solution Approach 1:
The patent establishes pre-configured templates and frameworks for common enterprise application patterns, business processes, and workload characteristics. These preliminary configurations can be quickly selected and adapted for specific testing scenarios, eliminating the need to build benchmarks from scratch while maintaining accuracy for standard enterprise patterns.
Solution Approach 2:
The testing system is designed with universal components that can serve multiple enterprise types and application patterns. A single automated framework can generate customized benchmarks for different industries and application scenarios by configuring parameters and selecting from reusable templates, reducing development time while maintaining enterprise-specific accuracy.
3Measurement precision
If more resources are allocated to cloud infrastructure testing, then performance evaluation quality improves, but resource costs and provisioning complexity increase
Solution Approach 1:
The patent implements automated self-service mechanisms for resource provisioning, configuration, and teardown. The system automatically discovers required resources, provisions them through APIs, configures testing environments, and cleans up after tests complete. This automation eliminates manual provisioning complexity while enabling comprehensive performance evaluation with appropriate resource allocation.
Solution Approach 2:
The system incorporates feedback loops that monitor resource utilization, performance metrics, and test results to dynamically adjust resource allocation. Based on feedback from previous tests and performance requirements, the system optimizes resource provisioning automatically, improving evaluation quality while preventing unnecessary resource expenditure and complexity.
Data Source
AI summary
In a computer-implemented method for automated test execution in a shared virtualized resource pool, a test description containing at least one model for a service under test (SUT) is received and one or more infrastructure configurations to be tested by resources in the shared virtualized resource pool based upon the test description are identified. In addition, a service lifecycle management (SLiM) tool is interacted with to cause the SUT and a load source to be created, the SLiM tool is directed to instantiate the SUT and the load source on the one or more infrastructure configurations in the shared virtualized resource pool for the SUT, and the SLiM tool and the load source are interacted with to receive performance data related to performance of the SUT under one or more loads generated by the load source.