Edge Computing Node Clustering to Reduce Redundant Transmission
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Solution Overview
Problem
Managing services in cloud-based information processing systems is challenging due to the dynamic nature of user needs and the varying performance requirements of applications, leading to inefficiencies in resource utilization and data transmission.
Innovation Solution
Implementing edge computing nodes with knowledge sharing mechanisms that cluster similar nodes based on extracted knowledge parameters, allowing for efficient operation and reduced resource consumption by sharing data and analysis results among groups of edge computing nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If edge computing nodes operate independently without knowledge sharing, then each node maintains operational autonomy and decision-making independence, but redundant data transmission and computation occur across the network
Solution Approach 1:
The patent merges edge computing nodes into clusters based on knowledge parameter similarity, enabling them to share computational tasks and data. This combination reduces redundant operations across the network while maintaining functional autonomy within each cluster, directly addressing the energy loss issue.
Solution Approach 2:
The system dynamically changes the operational parameters of edge nodes by adjusting their knowledge parameters and cluster assignments. This allows nodes to adapt their behavior based on similarity metrics and shared knowledge, optimizing energy consumption without requiring complex centralized control.
2Loss of information
If all edge computing nodes share all knowledge parameters with each other, then comprehensive knowledge availability is achieved, but communication overhead and data transmission costs increase significantly
Solution Approach 1:
The patent applies local quality by creating heterogeneous clusters where nodes share knowledge parameters selectively based on their similarity within specific clusters. Each cluster has its own knowledge sharing scope, ensuring information availability is optimized locally without requiring global knowledge exchange, thus reducing communication overhead.
Solution Approach 2:
The system segments the edge computing network into multiple clusters based on knowledge parameter similarity. This segmentation allows knowledge sharing to be confined within clusters rather than across the entire network, reducing communication overhead while maintaining sufficient information availability for each segment's operational needs.
3Productivity
If edge computing nodes are clustered based on knowledge parameter similarity, then resource efficiency is improved through reduced redundancy, but the complexity of cluster management and knowledge parameter extraction increases
Solution Approach 1:
The patent implements self-service by enabling edge nodes to autonomously extract their own knowledge parameters and determine their cluster assignments based on similarity metrics. This self-organizing approach reduces the need for centralized management complexity while improving resource efficiency through automated clustering.
Solution Approach 2:
The system uses parameter changes in knowledge extraction and similarity calculation to dynamically adjust cluster formations. This allows the system to adapt to changing operational conditions and node characteristics, improving resource efficiency without requiring manual reconfiguration or complex centralized control mechanisms.
4Measurement precision
If knowledge parameters are extracted and shared among edge computing nodes, then decision-making accuracy is improved through smart knowledge sharing, but the processing time and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-extracting and storing knowledge parameters at each edge node before clustering is needed. This preparation work is done in advance, so when clustering and knowledge sharing occur, the actual processing time is minimized while still achieving improved decision-making accuracy through the shared knowledge.
Solution Approach 2:
The system uses partial action by extracting and sharing only the most relevant knowledge parameters needed for decision-making, rather than all possible parameters. This selective approach maintains decision-making accuracy while reducing the computational overhead and processing time associated with handling excessive data.
Data Source
AI summary
An apparatus comprises a processing device configured to extract, from edge computing nodes in an edge computing environment, sets of knowledge parameters characterizing information generated by additional processing devices associated with the edge computing nodes. The processing device is also configured to generate groups of the edge computing nodes utilizing at least one clustering algorithm that takes into account the sets of knowledge parameters extracted from the edge computing nodes. A given group of the edge computing nodes comprises at least two edge computing nodes having respective sets of extracted knowledge parameters exhibiting at least a threshold level of similarity with one another. The processing device is further configured to control operation of the at least two edge computing nodes in the given group based at least in part on sharing knowledge parameters in the sets of knowledge parameters extracted from the at least two edge computing nodes.