Cluster Capability Intersection for Task Matching
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Solution Overview
Problem
Managing automated environments in network environments with diverse devices is challenging due to the complexity of processing tasks and varying device capabilities, as existing technologies struggle to effectively select the most capable clusters for task execution.
Innovation Solution
The method involves identifying leader devices within clusters, deriving cluster capability information by intersecting device capabilities, and dynamically updating this information to ensure accurate selection of clusters for task execution based on request parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device capabilities are aggregated to form cluster capabilities, then task selection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and transmits only the essential capability information (capability vectors) from individual devices to cluster leaders, rather than transmitting all raw device data. This extraction enables accurate cluster capability computation while reducing the complexity of data processing and communication in the system.
Solution Approach 2:
The system performs preliminary computation of cluster capabilities by having each cluster leader aggregate and compute the intersection of device capabilities within their cluster before task assignment. This preliminary action distributes the computational burden and pre-processes capability information, reducing real-time computational complexity during task selection.
2Measurement precision
If real-time capability updates are performed, then task assignment accuracy is improved, but system responsiveness decreases
Solution Approach 1:
The patent implements a feedback mechanism where devices signal their availability status and capability changes to cluster leaders. This feedback loop enables accurate real-time capability updates without requiring constant system-wide recomputation, maintaining both accuracy and responsiveness by updating only affected clusters.
Solution Approach 2:
The system dynamically adapts its behavior based on device availability and capability changes. When devices join or leave clusters, or when capabilities change, the system dynamically updates cluster capabilities only for affected clusters rather than performing global updates. This dynamic approach maintains accuracy while minimizing time loss through selective updates.
3Measurement precision
If cluster capabilities are computed by intersecting device capabilities, then task matching precision is improved, but processing time increases
Solution Approach 1:
The patent segments the capability computation task by having each cluster leader independently compute the capability intersection for their specific cluster. This segmentation divides the large computation into smaller, parallelizable tasks across multiple cluster leaders, improving processing time while maintaining the precision of capability matching through accurate intersection computation.
Data Source
AI summary
Techniques are disclosed for generating device cluster capability information for a cluster of devices in a network environment. Capability information can specify capabilities of the devices in the cluster. A first user device can generate device capabilities for the first user device and obtain device capabilities for other devices in the cluster. The first user device can generate cluster capability information providing an intersection of the first set of device capabilities and device capabilities of the other user devices in the cluster. The first user device can obtain cluster capability information for other clusters in the network environment and receive a request from a service user device to perform a specific task. The first user device can transmit cluster capability information relating to a selected cluster that corresponds with the request.