IoT Code Migration Using Operational Windows to Limit Disruption
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Solution Overview
Problem
In IoT environments, businesses face challenges in identifying optimal times for software updates on IoT devices to minimize disruptions and ensure smooth transitions.
Innovation Solution
A cloud-based system server analyzes operational information from IoT devices to identify an optimal window for code migration, ensuring compatibility and minimizing disruptions by simulating the code deployment on a replica before transferring it to the device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software updates are pushed to IoT devices without analyzing operational information, then update speed is improved, but user disruption increases and reliability deteriorates
Solution Approach 1:
The system performs preliminary actions by analyzing operational information and identifying optimal migration windows before actually pushing the code update. This allows the system to prepare and schedule updates during periods of low usage, thereby reducing user disruption while maintaining efficient update processes.
Solution Approach 2:
The system continuously monitors operational information from IoT devices and uses this feedback to dynamically determine the best timing for code migration. By listening to device operational status and usage patterns, the system can schedule updates when they will cause minimal disruption, resolving the contradiction between update speed and reliability.
2Ease of manufacture
If code migration is performed without identifying optimal timing windows, then deployment simplicity is improved, but functional correctness deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing operational information and determining optimal migration windows without requiring manual intervention. This automated approach maintains deployment simplicity while ensuring functional correctness through data-driven timing decisions.
Solution Approach 2:
The system changes the parameter of timing by dynamically selecting optimal migration windows based on operational information. This parameter adjustment allows the system to maintain simple deployment processes while improving functional correctness by choosing the best moments for code migration.
3Device complexity
If operational information is not collected and analyzed, then system complexity is reduced, but ability to minimize disruption worsens
Solution Approach 1:
The system introduces an intermediary layer that collects and analyzes operational information from IoT devices. This intermediary mechanism, implemented through code migration modules that monitor and evaluate operational data, enables the system to minimize user disruption without significantly increasing overall system complexity.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for code migration and deployment in an Internet-of-Things (IoT) environment. A system server (e.g., a server of a cloud-based platform, etc.) may receive a codebase and operational information for a target device (e.g., an IoT device, a mobile device, a smart device, etc.). The codebase may be modified for compatibility with the target device based on functionality for libraries of the codebase mapped to functional elements that define the functional capabilities of the target device. A codebase migration window may be identified based on an indication that an operation of the target device satisfies an operational threshold and the operational information received from the target device. The modified codebase may be transferred to the target device during the codebase migration window.