Radio Network Self-Healing via Community Model Prioritization
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Solution Overview
Problem
Radio-based networks face challenges in maintaining high availability and resilience due to manual configuration requirements, downtime issues, and lack of prioritization in self-healing approaches, especially when multiple failures occur concurrently.
Innovation Solution
The implementation of service function chaining using network service headers and the Girvan-Newman algorithm to identify prioritization of network connections, combined with state synchronization and dynamic reconfiguration, enhances resilience and self-healing in radio-based networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration methods are used for network recovery, then system complexity is reduced, but availability and recovery time deteriorate
Solution Approach 1:
The system implements automated self-healing capabilities where the network automatically detects failures, identifies alternative paths using the community model, and reconfigures connections without manual intervention. The orchestrator autonomously manages the recovery process, allowing the network to service itself and eliminate downtime caused by manual configuration.
Solution Approach 2:
The community model pre-identifies critical connections and alternative paths before failures occur. By establishing prioritization relationships and backup routes in advance, the system can immediately activate pre-planned recovery actions when failures happen, significantly reducing recovery time compared to manual configuration approaches.
2Loss of time
If automated self-healing is implemented, then recovery time is reduced, but differentiation between critical and non-critical connections deteriorates
Solution Approach 1:
The community model assigns different priority levels and recovery strategies to different connections based on their criticality to network functionality. Critical connections receive immediate attention and resources, while non-critical connections use standard recovery procedures. This localized differentiation ensures that automated healing processes focus resources on the most important connections first.
Solution Approach 2:
The system continuously monitors network health and connection status, using feedback from the community model to dynamically adjust recovery priorities. When failures are detected, the orchestrator receives feedback about which connections are most critical and allocates recovery resources accordingly, ensuring that prioritization is maintained throughout the automated healing process.
3Productivity
If all connections are treated equally in self-healing, then system simplicity is maintained, but productivity of critical connections deteriorates
Solution Approach 1:
The network connections are segmented into different communities based on their functional relationships and criticality. The community model divides the network into logical groups, allowing the orchestrator to apply different recovery strategies to different segments. This segmentation enables efficient prioritization of critical connections without requiring complex individual analysis of each connection.
Solution Approach 2:
The community model serves multiple functions: it identifies critical connections, determines prioritization, guides resource allocation, and coordinates recovery actions. This multi-functional approach consolidates several complex tasks into a single unified framework, maintaining system simplicity while enabling sophisticated prioritization and efficient recovery of critical connections.
Data Source
AI summary
Disclosed are various embodiments for improving self-healing and resiliency of radio-based networks through the use of a community model. In one embodiment, a community model graph representing a radio-based network is generated. Individual nodes of the community model graph correspond to one or more network functions of the radio-based network. Individual edges of the community model graph correspond to a respective network connection between the network function(s) of the radio-based network. The individual edges are assigned a respective measure of relative importance. An adverse health event is detected that impacts a plurality of network connections between network functions in the radio-based network. Self-healing activities for the network connections are prioritized based at least in part on the respective measures of relative importance corresponding to individual network connections.