Mesh Deployment Intelligence for Dependency and Anomaly Handling
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Solution Overview
Problem
Current software deployment tools lack the capability to manage complex dependencies, require manual intervention, and fail to detect errors early, leading to inefficiencies and delays in the Software Development Life Cycle (SDLC).
Innovation Solution
An automated mesh service-based deployment intelligence system integrating AI and machine learning to manage dependencies, schedule deployments, detect errors, and optimize processes across multiple environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current orchestration tools are used for software deployment, then basic deployment automation is achieved, but the system cannot manage complex deployment dependencies, coordinate across multiple teams, or handle time-dependent approval processes
Solution Approach 1:
The patent segments the deployment process into distinct phases (development, testing, staging, production) with specific approval gates between them. Each phase has defined entry and exit criteria, allowing complex dependencies to be managed through structured segmentation rather than monolithic process control.
Solution Approach 2:
The patent introduces an intermediary approval mechanism that mediates between different teams and deployment stages. This intermediary layer handles time-dependent approvals by allowing designated approvers to review and authorize transitions between phases, coordinating across multiple teams without requiring direct complex inter-team communication.
2Productivity
If manual deployment processes are used to handle approvals and coordination, then flexibility in managing dependencies is achieved, but inefficiencies, errors, and delays occur
Solution Approach 1:
The patent implements self-service deployment capabilities where the system automatically manages routine deployment tasks, artifact promotions between environments, and status tracking. This self-service automation eliminates manual errors while maintaining controlled flexibility through predefined approval workflows, simultaneously improving productivity and reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically monitor deployment status, track approvals, and provide real-time visibility into the deployment pipeline. This feedback loop enables early error detection and allows the system to automatically adjust or halt deployments when issues are detected, reducing errors while maintaining fast delivery speeds.
3Adaptability or versatility
If existing tools are used for deployment scheduling, then basic recurring deployment support is provided, but the system lacks flexibility for on-demand deployments and parallel deployment management
Solution Approach 1:
The patent implements dynamic deployment scheduling that can adapt between recurring scheduled deployments and on-demand trigger-based deployments. The system dynamically adjusts deployment timing and sequencing based on real-time conditions, artifact availability, and approval status, providing maximum scheduling flexibility without requiring manual coordination for each change.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring deployment pipelines, approval workflows, and environment promotions before actual deployments occur. This preliminary setup enables the system to quickly execute both recurring and on-demand deployments without requiring time-consuming coordination during the actual deployment event, reducing deployment coordination time while maintaining flexibility.
Data Source
AI summary
Systems, computer program products, and methods for an automated mesh service-based deployment intelligence system are provided. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.