Manifest Trialing for Cloud Update Deployment Efficiency
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Solution Overview
Problem
During cloud updates for large fleets of devices, some devices may fail to accept and deploy updates due to various reasons such as storage space issues or network outages, leading to inefficient and inconvenient multiple update campaigns.
Innovation Solution
Implementing a manifest trialing technique where a trial manifest is sent to devices before the actual update, allowing them to report acceptability, thereby determining the number of devices that can be updated and modifying manifest parameters to improve deployment rates, ensuring efficient update campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a cloud update is executed for a large fleet of devices, then the update deployment process is initiated, but some devices may fail to accept and deploy the update due to compatibility issues, storage space, or network outages
Solution Approach 1:
The manifest is sent to devices in advance of the actual update content, allowing devices to evaluate compatibility and report back before the full update is deployed. This preliminary action prevents wasting bandwidth on incompatible devices and identifies issues before they cause deployment failures.
Solution Approach 2:
Devices report their manifest evaluation results back to the cloud server, providing feedback on compatibility and potential issues. This feedback loop enables the server to identify failing devices and adjust the update campaign accordingly, improving overall deployment reliability.
2Reliability
If multiple update campaigns are executed to reach failing devices, then more devices can be updated, but the process becomes inefficient and difficult to implement universally
Solution Approach 1:
By evaluating device compatibility with the manifest before deploying the full update, the system identifies potentially failing devices in advance. This allows for targeted follow-up campaigns only for devices that reported issues, rather than running multiple campaigns for the entire fleet.
Solution Approach 2:
The fleet of devices is segmented into those that are compatible and ready for update, and those that reported issues and may need follow-up campaigns. This segmentation allows efficient resource allocation and reduces the time lost to repeated full-fleet campaigns.
3Reliability
If the manifest is sent to all devices, then compatibility can be evaluated, but network bandwidth is consumed even for devices that will not accept the update
Solution Approach 1:
The manifest is a lightweight, partial representation of the full update content. It provides sufficient information for compatibility evaluation without consuming the full bandwidth that would be required for the complete update payload, especially for devices that will ultimately reject the update.
Data Source
AI summary
Various implementations described herein are directed to a method for acquiring a manifest having a trial flag with a status indicator and providing the manifest to a plurality of devices. The status indicator may inform the plurality of devices to provide update acceptability reports. The method may include receiving the update acceptability reports from the plurality of devices and determining an update deployment rate based on the update acceptability reports received from the plurality of devices. The method may include deploying the update in accordance with the update deployment rate.