Synergy Aware Path Creation in Network Computing
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Solution Overview
Problem
In edge computing networks, far edge devices often struggle to collaborate effectively due to communication barriers, leading to redundant data collection and processing inefficiencies.
Innovation Solution
A computer-implemented method where processor units identify computing device groupings based on synergy levels, instruct them to share common data types, and deploy relay devices to facilitate communication between groupings that are not initially in communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If relay devices are deployed to facilitate communication between computing device groupings, then communication capability is improved, but device complexity increases
Solution Approach 1:
Relay devices are deployed as intermediary components to establish communication paths between computing device groupings that cannot directly communicate. The relay devices act as mediators that receive, process, and forward data packets between isolated groupings, enabling collaboration without requiring direct peer-to-peer connectivity.
Solution Approach 2:
The network is segmented into multiple computing device groupings, each capable of independent operation. Relay devices are strategically positioned to bridge these segments, allowing the system to maintain modular architecture while enabling inter-group communication when needed.
2Productivity
If computing device groupings share common data types, then processing efficiency is improved, but data management complexity increases
Solution Approach 1:
A unified data sharing mechanism is implemented that allows multiple computing device groupings to access and process the same set of common data types through standardized interfaces. This universal approach enables different groupings to benefit from shared data without each grouping needing to implement its own unique data management solution.
Solution Approach 2:
The system implements feedback mechanisms where computing device groupings report their data processing needs and capabilities. Based on this feedback, the system dynamically determines which groupings should share which data types, optimizing processing efficiency while managing data distribution complexity.
3Speed
If edge devices process data locally, then response time is improved, but bandwidth usage increases due to redundant data collection
Solution Approach 1:
Multiple edge computing device groupings that process the same types of data are merged into collaborative units. Instead of each grouping independently collecting and processing identical data, they share common data types through relay devices, reducing redundant data collection and transmission while maintaining local processing capabilities for fast response times.
Data Source
AI summary
A computer implemented method for computing device collaboration. A number of processor units identify computing device groupings for collaboration in processing data based on synergy levels between the computing device groupings. The computing device groupings process a set of common data types. The number of processor units instruct the computing device groupings to share the data for the set of common data types. The number of processor units deploy a number of relay devices to facilitate communications between the computing device groupings in response to the computing device groupings not being in communication with each other.