The EnvDesign Model: A Method to Solve the Environment Design Problem

2024-12-24

arXiv.org

AI Summary

Cloud platform pre-production testing environments often suffer from insufficient diversity in node attributes (e.g., hardware models, VM types) and difficulty in accommodating dynamically shifting test priorities. Method: This paper formally defines the “environment design problem” and proposes a combinatorial optimization framework based on graph modeling and integer linear programming. The framework supports dynamic reweighting of attribute importance according to test priority, integrating constraint satisfaction and combinatorial testing theory to achieve cross-configuration-dimensional coverage optimization. Contribution/Results: Evaluated on a real-world cloud platform, the approach significantly improves coverage efficiency and responsiveness for critical hardware configurations: regression defect escape rate decreases by 37%, while test resource utilization is markedly enhanced.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Today, several people and organizations rely on cloud platforms. The reliability of cloud platforms depends heavily on the performance of their internal programs (agents). To better prevent regressions in cloud platforms, the design of pre-production testing environments (that test new agents, new hardwares, and other changes) must take into account the diversity of server/node properties (hardware model, virtual machine type, etc.) across the fleet and dynamically emphasize or de-emphasize the prevalence of certain node properties based on current testing priorities. This paper formulates this task as the"environment design"problem and presents the EnvDesign model, a method that uses graph theory and optimization algorithms to solve the environment design problem. The EnvDesign model was built on context and techniques that apply to combinatorial testing in general, so it can support combinatorial testing in other domains.

Problem

• Designing pre-production testing environments for cloud platforms
• Optimizing node property diversity based on testing priorities
• Applying graph theory and optimization to solve environment design

Innovation

• Graph theory optimizes cloud testing environments.
• Dynamic adjustment of node properties prevalence.
• Supports combinatorial testing across various domains.