Home Function Entity Redundancy for Network Reliability
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Solution Overview
Problem
Current network technologies lack reliability in ensuring continuous service and quality of service (QoS) due to the absence of effective redundancy mechanisms, particularly in networks with separate bearer control layers, leading to service interruptions and inadequate load balancing when function entities fail.
Innovation Solution
Implementing a method where multiple home function entities with identical characteristics are distributed across the network, allowing them to intercommunicate and take over services in case of failure, ensuring continuity and improving load balancing through a master/backup or load sharing manner, using heartbeat signals and protocol connections for real-time detection and switching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cold backup method is used for reliability, then service continuity is improved, but network complexity and service interruption time increase
Solution Approach 1:
The network entities are segmented into primary and backup roles, with each entity having clearly defined functions. The backup entity monitors the primary entity through heartbeat signals and takes over only when needed, dividing the reliability function into discrete monitoring and execution components rather than requiring complete system redundancy.
Solution Approach 2:
The backup entity is pre-configured with all necessary routing information and service context before failure occurs. Heartbeat monitoring is established in advance, and the backup entity maintains readiness to take over services without requiring post-failure configuration or service reconstruction, enabling immediate failover.
2Reliability
If multiple home function entities are deployed for load balancing, then service reliability is improved, but network complexity increases
Solution Approach 1:
Multiple home function entities are merged into a unified reliability framework where primary and backup entities share the same service context and configuration. The entities work as a coordinated pair rather than independent systems, with the backup entity mirroring the primary entity's state, reducing overall system complexity despite having multiple instances.
Solution Approach 2:
The backup entity is created as a copy of the primary entity with identical service context, routing information, and configuration. This copying approach allows the backup to immediately assume the primary's role without requiring complex reconstruction logic, simplifying the reliability mechanism while enabling load balancing and failover capabilities.
3Productivity
If conventional Diff-Serv model is used, then line utilization is improved, but QoS guarantee reliability deteriorates
Solution Approach 1:
The system establishes backup entities and heartbeat monitoring mechanisms in advance to cushion against potential failures. When a primary entity fails, the pre-positioned backup entity with maintained service context can immediately take over, providing a cushion against QoS degradation while the system continues to operate at high line utilization without requiring over-provisioning.
Data Source
AI summary
A method for ensuring reliability in a network is disclosed, including: distributing, based on service flow, services to multiple home functions entities of the coupled function entity, where the multiple home function entities have identical functional characteristics in the network; taking over, by a first home function entity operating normally, work of a second home function entity to be switched if determining to switch between the multiple home function entities. Through the method, if one or more of the multiple home function entities is in failure, other home function entities may take over work of the function entities in failure, which ensures the continuity of data flows of services and prevents the services from being interrupted, greatly improves reliability and load balancing ability of end-to-end QoS architecture. Furthermore. There is no limitation on network architecture and the method is applicable to networks with any scales.