Telecom Load Balancer Decrypts S1-MME Traffic for Node Distribution
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Solution Overview
Problem
Existing load balancing systems for network traffic monitoring struggle to efficiently distribute traffic across multiple monitoring nodes, especially when mobile user devices change identifiers, leading to potential overload or underutilization of nodes and difficulties in maintaining effective monitoring.
Innovation Solution
A monitoring node load balancer that decrypts encrypted S1-MME traffic using information from S6a traffic, correlates subscriber identifiers, and distributes tapped traffic across multiple monitoring nodes using these identifiers, ensuring balanced traffic distribution regardless of whether traffic is associated with an IMSI or GUTI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If load balancing is implemented among multiple network analysis tools, then traffic distribution efficiency is improved, but maintaining consistent monitoring for mobile users with changing identifiers becomes difficult
Solution Approach 1:
The system performs preliminary actions by capturing and correlating subscriber identifiers (IMSI and GUTI) during the initial attachment phase. This creates a binding record before the mobile user moves between base stations, enabling the load balancer to maintain consistent routing even as identifiers change during mobility
Solution Approach 2:
The load balancer acts as an intermediary component that sits between the network traffic and multiple network analysis tools. It receives traffic, consults the binding table to determine the appropriate monitoring node, and forwards traffic accordingly, thereby decoupling the load distribution function from the monitoring consistency requirement
2Reliability
If all packets for a mobile user are forwarded to the same network analysis tool, then monitoring effectiveness is improved, but node overload may occur
Solution Approach 1:
The system segments the traffic flow by creating bindings between specific subscriber identifiers and specific monitoring nodes. Instead of all traffic going to a single node, the load balancer distributes different subscribers' traffic to different monitoring nodes based on the binding table, achieving both monitoring effectiveness and load distribution
Solution Approach 2:
The system changes the routing parameter from static (fixed monitoring node assignment) to dynamic (identifier-based binding). By using subscriber identifiers as the routing key, the system can adapt traffic distribution to maintain monitoring effectiveness while preventing any single node from becoming overloaded
3Measurement precision
If encrypted S1-MME traffic is decrypted for load balancing, then traffic distribution accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts only the necessary subscriber identifier information from the encrypted S1-MME traffic that is required for load balancing decisions. Rather than decrypting and processing entire traffic flows, it selectively extracts identification data to populate the binding table, reducing the complexity burden while maintaining distribution accuracy
Data Source
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AI summary
The subject matter described herein relates to methods, systems, and computer readable media for test system connection resiliency. In some examples, a method for distributing monitored network traffic includes monitoring S1 -MME traffic and S6a traffic in a core network of a telecommunications network. The method includes decrypting encrypted S1 -MME traffic using information extracted from the S6a traffic. The method includes load balancing between a plurality of monitoring nodes using the decrypted S1 -MME traffic.