Border Router Routing Tables Optimized by Convex Pricing and Latency
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Solution Overview
Problem
Border network routing table updates in communication networks are complicated by the integration of third-party routers, leading to potential network degradation and packet loss due to reactive alert-based corrections rather than proactive management.
Innovation Solution
A routing table update server analyzes weighted parameters like transmission price, capacity, and performance to continuously update border router tables, using convex optimization and machine learning to maintain network optimality and prevent congestion or performance impairments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reactive alert-based corrections are used for routing table updates, then device complexity is reduced, but network reliability deteriorates due to packet loss and network degradation
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network parameters (latency, packet loss, bandwidth utilization) and proactively adjusting routing tables before network degradation occurs. The routing table update server calculates optimal routes in advance based on current network conditions and updates routing tables preemptively, preventing packet loss and network degradation rather than reacting to alerts after problems occur.
2Reliability
If continuous optimization with multiple parameters is implemented, then network reliability improves, but device complexity increases due to multiple monitoring and calculation components
Solution Approach 1:
The routing table update server performs multiple functions: it monitors network parameters (latency, packet loss, bandwidth), calculates optimal routes using convex optimization, updates routing tables, and prevents both congestion and underutilization. This multi-functional approach consolidates what would otherwise require separate systems into a single universal component, improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system implements continuous feedback loops where network performance metrics are monitored, routing decisions are made based on convex optimization of these metrics, and the results are fed back into the optimization process. This closed-loop feedback mechanism enables the system to adapt to changing network conditions dynamically, maintaining high reliability through continuous optimization rather than static routing tables.
3Reliability
If proactive continuous updates are performed, then network reliability improves, but loss of time increases due to continuous monitoring and calculation
Solution Approach 1:
The system performs routing table updates periodically based on changing network conditions rather than continuously without interruption. The convex optimization process calculates optimal routes at intervals when significant changes in network parameters are detected, balancing the need for proactive optimization with efficient use of processing time. This periodic action prevents unnecessary continuous calculations while maintaining network performance.
Data Source
AI summary
In a border network having a plurality of routers, routing tables for the routers can be generated using one or more weighted parameters, such as a price associated with transmission of packets over third-party routers, capacity of the border routers, and performance information (e.g., latency) of the border routers. The routing tables can be repetitively generated to proactively ensure that the border network is operating optimally. The framework described augments a control plane with Availability, Performance, and Cost (APC) telemetry collected from existing services in a cloud environment. Such telemetry can be continuously correlated in a convex optimization framework to maintain the network in its optimal state. As a result, the framework can proactively avoid potential network degradation events (e.g. congestion) and the resulting customer impact, while reducing the cost of routing traffic through upstream peers/transits.