Entropy-Based Stability Management for Mesh Network Clustering
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Solution Overview
Problem
Existing network management systems face challenges in scalability and stability, particularly in decentralized networks like mesh networks, where centralized models fail to scale and decentralized models lack effective control and management mechanisms, leading to complexity and inefficiency.
Innovation Solution
The implementation of an entropy-based self-organizing stability management system that forms and aggregates clusters hierarchically, using virtual clustering and distributed virtual machines to balance centralized and decentralized control, enabling nodes to self-promote and demote based on stability metrics, thereby optimizing network performance and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized management is used, then control and management authority is consolidated, but scalability to very large complex systems deteriorates
Solution Approach 1:
The patent segments the network into hierarchical clusters with cluster-heads at multiple levels. Each cluster operates semi-autonomously while being part of the larger network structure, allowing the system to scale to very large sizes while maintaining centralized coordination through the hierarchical cluster-head architecture.
Solution Approach 2:
The patent introduces a hierarchical dimension to the network structure, organizing nodes into multiple levels of clusters (Level-0, Level-1, Level-2, etc.). This dimensional organization allows centralized control to be distributed across hierarchical levels, enabling scalability while preserving control authority.
2Adaptability or versatility
If decentralized management is used, then system autonomy is improved, but control and management complexity increases
Solution Approach 1:
The patent introduces cluster-heads as intermediary entities that mediate between individual nodes and the broader network. Cluster-heads provide local control and coordination, reducing the complexity burden on individual nodes while maintaining overall network autonomy through distributed cluster-head management.
Solution Approach 2:
The network is segmented into autonomous clusters, each managed by a cluster-head. This segmentation allows decentralized autonomy at the cluster level while reducing individual node complexity, as nodes only need to interact with their cluster-head rather than the entire network.
3Reliability
If nodes operate independently in peer-to-peer networks, then system robustness is improved, but management and control efficiency deteriorates
Solution Approach 1:
The patent segments the peer-to-peer network into hierarchical clusters, maintaining node independence and robustness within clusters while improving management efficiency through cluster-head coordination. The hierarchical structure enables efficient resource allocation and management decisions at appropriate levels.
Solution Approach 2:
The patent implements dynamic cluster formation and node promotion/demotion based on operational performance. Nodes can be promoted to cluster-head status or demoted based on metrics like stability and reachability, creating a dynamic system that maintains robustness while improving management efficiency through adaptive organization.
4Productivity
If virtual clustering with self-promotion is implemented, then network performance is optimized, but computational overhead increases
Solution Approach 1:
The patent uses parameter-based decisions for node promotion and demotion, monitoring metrics such as stability, reachability, and operational performance. By making promotion decisions based on measurable parameters rather than complex computations, the system optimizes network performance while controlling computational overhead.
Solution Approach 2:
Nodes autonomously monitor their own performance metrics and can self-promote to cluster-head status or self-demotion based on predefined criteria. This self-service mechanism optimizes network performance through distributed decision-making while minimizing computational overhead by avoiding centralized evaluation of all nodes.
Data Source
AI summary
In some embodiments, the invention involves calculating entropy-based stability values to be used in a framework to build a new class of network control (policy) and (state) management services. The framework may be used to build a number of self-management services to support decentralized (mesh) networks. Other embodiments are described and claimed.