Bookkeeping Nodes Manage Code Update Deployment Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional code update deployment methods from a central server are inefficient, particularly in large computing environments, as they are time-consuming, unpredictable, and can result in failed updates due to scripted procedures and reboot requirements, with no control over when updates occur.
Innovation Solution
A deployment environment with bookkeeping nodes that manage deployment parameters such as maximum allowable deployment time, number of active deployments, and failure rates to dynamically determine when and how code updates are applied, allowing for throttling and risk adjustment based on success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional push-based code update deployment is used, then updates can be distributed from a central server, but the process is time-consuming and inefficient in large computing environments
Solution Approach 1:
The patent segments the deployment process into multiple phases: testing phase with limited nodes, gradual rollout phase with increasing nodes, and full deployment phase. This segmentation allows the system to manage deployment complexity and control timing, resolving the contradiction between efficient deployment and time consumption by implementing staged rollouts that prevent simultaneous updates across all nodes.
Solution Approach 2:
The patent implements dynamic deployment rate adjustment based on real-time monitoring of update success rates. The system automatically modulates the deployment rate parameter during different phases, increasing it when success rates are high and decreasing it when failures are detected. This dynamic control optimizes deployment efficiency while managing time by adapting to actual system conditions.
2Reliability
If scripted update procedures are used to automate deployments, then updates can be applied systematically, but the process becomes unpredictable and may fail due to reboot requirements
Solution Approach 1:
The patent performs preliminary testing of code updates on a limited set of nodes before full deployment. This preliminary action phase identifies potential issues and validates update procedures in advance, improving reliability by catching problems before they affect the entire system and making the overall process more predictable.
Solution Approach 2:
The patent implements continuous feedback mechanisms that monitor update success rates, failure modes, and system behavior during deployment. This feedback is used to dynamically adjust deployment rates and identify patterns that may indicate predictable failure conditions, allowing the system to respond proactively to maintain reliability and predictability.
3Speed
If code updates are pushed to all nodes simultaneously, then deployment speed is maximized, but the number of active deployments becomes unmanageable and failure risk increases
Solution Approach 1:
The patent applies partial action by deploying updates to only a subset of nodes at any given time rather than all nodes simultaneously. The system maintains a manageable number of active deployments by limiting the scope of each deployment wave, reducing management complexity while still achieving progress toward full deployment.
Solution Approach 2:
The patent changes the deployment rate parameter dynamically during different phases. In the testing phase, the rate is low with few nodes; in the gradual rollout phase, the rate increases moderately; and in the full deployment phase, the rate is maximized. This parameter change strategy balances deployment speed with management complexity by adapting the rate to the current state of the deployment process.
4Reliability
If manual update application is used, then each update can be carefully controlled, but the process becomes too time-consuming for large computing environments
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically monitors deployment status, tracks success rates, and adjusts deployment rates without manual intervention. This automation maintains the benefits of controlled updates while eliminating the time-consuming manual processes, allowing the system to self-regulate based on real-time conditions and scale efficiently across large computing environments.
Data Source
AI summary
One or more bookkeeping nodes may receive a request to deploy update information from a requesting node. The bookkeeping node(s) may determine whether to authorize the requesting node to deploy the update information based at least in part on one or more deployment parameters. If authorized, the requesting node may download the update information from one or more download nodes.