Network Resource Deployment via Multidimensional Clustering
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Solution Overview
Problem
Current network deployment systems face challenges in scalability and adaptability, especially when dealing with mobile infrastructure and real-time changes in network topology and conditions, limiting their ability to provide efficient resource allocation and network orchestration.
Innovation Solution
A method and system that divide a geographic zone into multidimensional horizontal clusters based on features like node quantity, position, and traffic type, assigning cluster heads and serving nodes while considering capacity and distance to avoid interference, and dynamically repositioning nodes based on thresholds and environmental changes, with self-tuning capabilities for cost optimization and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If centralized control mechanism or semi-distributed architectures are used to manage network resources, then dynamic resource allocation and network orchestration are provided, but scalability and ability to adapt to real-time changes in network topology and conditions are limited
Solution Approach 1:
The network is segmented into multiple autonomous clusters, each managed by a cluster head that independently makes deployment decisions. This segmentation enables local adaptability to real-time changes without requiring centralized control, resolving the contradiction between adaptability and system complexity by distributing intelligence across autonomous units.
Solution Approach 2:
The system implements dynamic cluster formation and reconfiguration based on real-time network conditions, node mobility, and traffic patterns. Cluster heads and serving nodes continuously adjust their positions and assignments adaptively, enabling the network to respond to topology changes without complex centralized reorchestration.
2Area of stationary object
If more serving nodes are deployed to cover larger geographic zones, then network coverage is improved, but wireless interference increases due to reduced distance constraints between nodes
Solution Approach 1:
The geographic zone is divided into multiple clusters with spatial separation between them. Each cluster is served by dedicated serving nodes, which reduces the number of active transmitting nodes in any given location and minimizes wireless interference while maintaining broad overall coverage through the distributed cluster structure.
Solution Approach 2:
Each cluster is configured with local optimization parameters including distance constraints specific to that cluster's density and traffic requirements. This allows the system to maintain appropriate node spacing locally to avoid interference while achieving extensive overall coverage through the aggregation of multiple clusters.
3Productivity
If cluster heads are assigned to manage multiple served nodes, then resource allocation efficiency is improved, but deployment delay increases due to capacity and positioning constraints
Solution Approach 1:
Cluster heads are pre-positioned in optimal locations based on predicted traffic patterns and network topology. This preliminary positioning reduces deployment delay when nodes need to be assigned or reconfigured, as the cluster heads are already in favorable positions to quickly manage their served nodes without requiring time-consuming repositioning.
Solution Approach 2:
The system dynamically adjusts cluster head capacity parameters and assignment limits based on real-time conditions. When deployment delay becomes critical, the system can modify cluster sizes and serving capacities to enable faster node assignment, balancing resource allocation efficiency with deployment speed.
Data Source
AI summary
A method and a system deploying resources in a network that comprises a plurality of network nodes composed of served nodes and serving nodes, the serving nodes comprising a plurality of cluster heads. A served geographic zone is divided into a plurality of multidimensional horizontal clusters based on a multi-dimensional vector of different features (number of the served nodes, position values of the served nodes, traffic flow type). One CH node is assigned to each multidimensional horizontal cluster considering capacity of the assigned CH node and deployment delay of the assigned CH node. One serving node is assigned to each multidimensional horizontal clusters considering capacity of the assigned serve node; required capacity to serve a particular one of the multidimensional horizontal clusters; and a distance constraint between two or more of the served nodes to avoid wireless-interference.