Moving Edge Capacity for Multi-Site Software Transition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing centralized data center infrastructure cannot deliver the speeds needed at the edge computing technology, making it challenging to move software installations between data centers, especially when there are connectivity challenges or when software is not continuously required in both environments.
Innovation Solution
A system that uses intelligent movement algorithms to transition software between data centers via movable edges, allowing for dynamic capacity docking, prefetching agents, and scheduling based on usage statistics and time periods, ensuring software is ready for operation upon connection to the target data center.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If software is installed in centralized data centers, then software management is simplified, but delivery speed to edge computing locations is insufficient
Solution Approach 1:
The patent implements prefetching agents that proactively identify and prepare software installations at edge locations before they are actually needed. By analyzing mobility patterns and predicting future software requirements, the system pre-positions software packages at edge data centers, eliminating delivery delays when devices arrive at new locations.
Solution Approach 2:
The patent introduces edge data centers as intermediary nodes between centralized software repositories and mobile devices. These edge locations serve as local software distribution points, caching frequently needed software and enabling rapid installation without direct connection to central data centers, thus improving delivery speed while managing complexity.
2Reliability
If software is continuously installed in all data centers, then software availability is improved, but licensing and maintenance costs increase
Solution Approach 1:
The patent implements location-specific software deployment by analyzing device mobility patterns and installing only the software that will actually be needed at each edge location. Instead of uniformly distributing all software to all data centers, the system tailors software packages to specific locations based on predicted usage, reducing unnecessary licensing costs while maintaining availability where needed.
Solution Approach 2:
The patent employs dynamic software distribution that adapts to changing conditions. The system continuously monitors device mobility patterns, location data, and software usage statistics to dynamically adjust which software is installed at which edge data center. This dynamic approach ensures software availability when and where needed while minimizing redundant installations and associated costs.
3Productivity
If edge computing locations are increased, then computational power near users is improved, but software installation and maintenance complexity increases
Solution Approach 1:
The patent implements self-service software installation at edge locations through automated agents that monitor incoming devices, identify software requirements based on mobility patterns, and autonomously install necessary software packages. This automation eliminates manual intervention at each edge location, reducing installation complexity despite the increased number of edge computing nodes.
Solution Approach 2:
The patent employs feedback mechanisms where agents at edge data centers continuously report device arrival patterns, software installation success rates, and usage statistics back to the central management system. This feedback loop enables the system to learn from actual deployments and optimize software distribution strategies, reducing complexity through data-driven decision making.
Data Source
AI summary
An approach for dynamically transitioning software associated with client devices in edge computing between one or more data centers is disclosed. The approach includes retrieving locations for one or more edges associated with one or more data centers; identifying data access location from client devices; determining mobility pattern associated with the data access by the client devices; identifying one or more data center services associated with the mobility pattern; determining one or more solutions associated with the mobility pattern based on an intelligent movement algorithm, the one or more data center services and the data access location; and applying the one or more solutions.