CI/CD Pipeline Workflow Deployment with Telemetry Instrumentation
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Solution Overview
Problem
As computing device ecosystems grow in complexity, determining the optimal deployment of workflows within these ecosystems becomes challenging due to the lack of data on how workflows will execute, making it difficult to efficiently deploy them in production environments.
Innovation Solution
A method using pipeline data for deploying workflows involves determining a pipeline testing trigger, decomposing a workflow, generating an execution plan, adding instrumentation, validating the plan, deploying computing devices in a CI/CD pipeline ecosystem, capturing deployment logs and telemetry, and providing deployment information to orchestrators for efficient deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workflows are deployed in complex device ecosystems without prior execution data, then deployment can proceed quickly, but deployment efficiency and optimality deteriorate due to lack of information on resource usage and performance
Solution Approach 1:
The patent applies preliminary action by executing workflows in a CI/CD pipeline ecosystem before production deployment to collect execution data. The system decomposes workflows into services, instruments them with telemetry capture, and gathers performance metrics and resource usage information in advance. This preliminary execution and data collection resolves the contradiction by ensuring execution data is available before actual deployment, enabling informed deployment decisions without sacrificing deployment speed.
2Measurement precision
If comprehensive pipeline testing and telemetry capture are implemented, then deployment information quality improves, but system complexity and resource requirements worsen
Solution Approach 1:
The patent applies segmentation by breaking down workflows into individual services and decomposing the pipeline manifest into manageable components. Each service is independently instrumented and tested, allowing comprehensive monitoring without overwhelming system complexity. The segmentation of deployment information into structured data sets (performance metrics, resource usage, telemetry) also helps manage complexity while maintaining high measurement precision.
Solution Approach 2:
The patent uses an intermediary approach by introducing a dedicated pipeline ecosystem that acts as a buffer between workflow development and production deployment. This intermediary environment captures telemetry data and generates deployment information sets without directly complicating the production system. The orchestrator serves as another intermediary that processes and utilizes the collected data, isolating complexity from the core deployment target.
3Loss of information
If workflows are decomposed and instrumented with telemetry capture, then resource usage and performance metrics are captured accurately, but deployment time and processing overhead increase
Solution Approach 1:
The patent applies continuity of useful action by making telemetry capture and data collection continuous throughout the workflow execution in the CI/CD pipeline. Rather than discrete sampling, the system continuously monitors resource usage, performance metrics, and execution behavior. This continuous collection ensures complete performance data without requiring multiple separate measurement phases, thereby reducing overall preparation time while maintaining data completeness.
Data Source
AI summary
Techniques described herein relate to a method for using pipeline data for deploying workflows. The method may include determining that a pipeline testing trigger occurred for a workflow; decomposing a pipeline testing manifest of the workflow; generating a testing execution plan using the decomposed workflow; adding instrumentation to the testing execution plan; determining that the instrumented testing execution plan is valid; deploying computing devices within a CI/CD pipeline ecosystem for performing the instrumented testing execution plan; capturing deployment logs; initiating telemetry capture; executing pipeline testing of the workflow based on the instrumented testing execution plan; generating a pipeline deployment information set based on the pipeline testing; and providing the pipeline deployment information set to an orchestrator of a production device ecosystem.