CI/CD Deployment Risk Assessment Using Distance Matrices
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Solution Overview
Problem
Existing deployment changes in computing systems, particularly through CI/CD pipelines, introduce significant risks such as service disruptions, latency, incorrect functionality, security vulnerabilities, and cascading effects, which current methods fail to adequately mitigate.
Innovation Solution
A risk estimation process using deployment data and Wasserstein distance matrices to quantify and compare differences between existing and proposed deployments, enabling early identification and management of health, stability, and security risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deployment changes are made frequently through CI/CD pipelines, then productivity and agility are improved, but reliability and system stability deteriorate due to increased risk of service disruptions and incorrect functionality
Solution Approach 1:
The system performs preliminary risk assessment by calculating distance matrices between current and proposed deployments before changes are applied. This early evaluation identifies potential issues with health, stability, and security risks, allowing teams to address problems before they manifest as service disruptions or functional failures during deployment.
Solution Approach 2:
The system establishes feedback loops by continuously monitoring deployment metrics and comparing them against baseline characteristics. The distance matrix calculation provides real-time feedback on the divergence between current deployment state and historical stable states, enabling dynamic adjustment of deployment strategies to maintain reliability while preserving productivity.
2Reliability
If comprehensive risk assessment is performed using distance matrices, then reliability and risk detection are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system creates a simplified mathematical representation (distance matrix) that copies the essential characteristics of complex deployment states into a manageable format. This matrix abstraction captures health, stability, and security metrics in a compressed form that can be efficiently compared and analyzed without requiring complex real-time simulation or exhaustive analysis of all system parameters.
Solution Approach 2:
The system transforms complex deployment state parameters into a standardized distance metric framework. By changing the representation from raw system parameters to normalized distance values, the system reduces computational complexity while maintaining assessment accuracy. The distance matrix converts multi-dimensional deployment characteristics into a single comparable metric that simplifies risk evaluation.
Data Source
AI summary
Methods and systems for managing deployments that provide computer-implemented services using one or more data processing systems are provided. A risk estimation process may be employed to identify and quantify difference(s) between two deployments (namely, an existing deployment and a new deployment) in a normalized and quantitative manner. Such difference(s) may be used to determine a level of risk associated with deploying the new deployment. This level of risk may then be compared against deployment policies of an entity to determine whether the new deployment may actually be deployed.