Mobile Core MME Priority Signaling for Geo-Redundant Load Balancing
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Solution Overview
Problem
Existing mobile core network architectures lack mechanisms to efficiently distribute traffic from geographically concentrated base stations to the nearest/local datacenter while ensuring datacenter resiliency, often requiring manual intervention during failures or capacity overload, leading to service interruptions.
Innovation Solution
Introduce a new information element (IE) in S1-Setup messages that includes priority configuration information, allowing base station entities to automatically select management entities based on local vs. remote datacenters, ensuring graceful switchover to backup entities during failures or capacity overload without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used during failures or capacity overload, then service interruptions can be managed, but response time increases and automation level decreases
Solution Approach 1:
The system pre-configures priority information for multiple management entities (MMEs) before failures occur. Base stations are预先 equipped with knowledge of which MMEs to contact first, second, third, etc., eliminating the need for manual intervention during failures. This preliminary configuration enables automatic failover selection based on pre-established priorities.
2Productivity
If traffic is distributed to remote datacenters, then load balancing is achieved, but latency increases
Solution Approach 1:
The system assigns different priority levels to MMEs based on their geographic proximity to base stations. Local MMEs receive higher priority designations, ensuring traffic is routed to the nearest available datacenter first. Remote MMEs are assigned lower priorities and serve as backup options, creating a hierarchical routing structure that optimizes for both load distribution and latency.
3Reliability
If geographic redundancy is implemented across extended areas, then resiliency improves, but system complexity increases
Solution Approach 1:
The system manages geographic redundancy by changing the priority parameter of different MMEs rather than creating complex routing rules. Each MME is assigned a simple priority value (first priority, second priority, third priority, etc.), transforming a complex geographic redundancy problem into a simple parameter-based selection process that base stations can easily implement.
4Loss of time
If automatic failover mechanisms are implemented, then response time decreases, but information overhead increases
Solution Approach 1:
Priority information is pre-configured and distributed to base stations before failures occur, stored as persistent configuration data. This preliminary action eliminates the need for real-time information exchange during failover events, reducing both response time and ongoing information overhead. The priority hierarchy is established once and reused across multiple failover scenarios.
Data Source
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AI summary
Techniques are presented in which a new information element signaling priority of a management entity is included in a setup (e.g., S1 -Setup) response or configuration update message sent by a management entity to a base station entity. The base station entity interprets this priority information along with the relative capacity information in an appropriate way to load-distribute the traffic/calls to highly preferable management entity instances (at a local site) when they are available, and switchover/failover to lower preference management entity instances (at a remote site) when there is a local site outage/failure or insufficient capacity in a geo-resilient pooled network.