Pre-distributing Software Artifacts to Cache Nodes
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Solution Overview
Problem
Modern software deployment in computing systems is complex and resource-intensive, requiring frequent updates and patches across multiple machines, often resulting in lengthy deployment windows that disrupt system operations and are prone to human errors, leading to increased costs and resource consumption.
Innovation Solution
A deployment automation system that pre-distributes software artifacts to cache devices located strategically near target devices, allowing for efficient and automated deployment within defined windows, reducing the need for direct access to target systems during the distribution phase and minimizing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artifacts are distributed directly to target devices during deployment, then deployment can be completed, but deployment time and resource consumption increase
Solution Approach 1:
The patent pre-distributes artifacts to cache devices before the actual deployment occurs. This preliminary action allows artifacts to be staged in advance at strategic cache locations, so that during the deployment window, only the final installation step is needed at target devices, dramatically reducing deployment time and resource consumption during the deployment window itself.
Solution Approach 2:
The patent introduces cache devices as intermediary nodes between the source and target devices. These cache devices store artifacts locally and serve them to target devices during deployment, eliminating the need for direct source-to-target communication during the deployment window and reducing network bandwidth consumption and target device resource usage.
2Ease of operation
If deployment requires direct access to target systems, then deployment can be performed, but system operation disruption increases
Solution Approach 1:
The system performs preliminary artifact distribution to cache devices before the deployment window begins. This allows the actual deployment to proceed quickly with minimal target system access time, reducing operational disruption while maintaining the ability to deploy to target systems when needed.
Solution Approach 2:
Cache devices act as intermediaries that hold artifacts ready for deployment. This reduces the duration and intensity of direct interactions with target systems, minimizing disruption to system operations while still enabling deployment when the target systems are accessible.
3Extent of automation
If manual deployment processes are used, then deployment can be controlled, but human errors increase
Solution Approach 1:
The system implements automated artifact distribution to cache devices and automated deployment execution. The deployment process serves itself by automatically managing artifact staging, distribution, and installation without manual intervention, thereby eliminating human errors while maintaining control through automated workflows and validation mechanisms.
Solution Approach 2:
The system incorporates feedback mechanisms that track artifact distribution status, deployment progress, and system states. This automated feedback loop ensures reliable deployment execution by monitoring each step and triggering appropriate actions or error handling without human intervention, improving both automation level and reliability.
Data Source
AI summary
A set of artifacts is identified for deployment on a target device in a deployment. The set of artifacts are from a source computing system remote from the target device. A cache device can be determined as corresponding to the target device, the cache device separate from the target device. The set of artifacts are pre-distributed on the cache device in advance of the deployment. The set of artifacts are sent to the cache device from the source computing system to be held at the cache device prior to the artifacts being distributed to the target device. The deployment follows distribution of the set of artifacts on the target device.