Optimized Edge Routing Master Node for Network Traffic Optimization
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Solution Overview
Problem
Conventional routing protocols, such as BGP, struggle to optimize inter-AS routing efficiently, especially in large networks, due to limitations in scaling and responding quickly to changes in network traffic patterns, leading to suboptimal bandwidth utilization and increased processing resources.
Innovation Solution
The Optimized Edge Routing (OER) technique employs a Master node that dynamically learns and aggregates address prefixes and statistics from border nodes, allowing for quicker and more efficient distribution of network traffic, using learn-filter criteria to filter and process only relevant data, thereby optimizing network traffic patterns at the edge of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional routing protocols (BGP) are used for inter-AS routing, then routing operations can be performed with simple protocol implementation, but the system cannot respond quickly to changes in network traffic patterns and struggles to optimize bandwidth utilization
Solution Approach 1:
The system segments routing optimization into two independent components: (1) OER border processes running on border routers that collect and export routing information, and (2) OER master process that analyzes exported information and generates routing policies. This segmentation allows fast local response at border routers while centralized optimization at the master level, resolving the contradiction between quick response and optimization complexity.
Solution Approach 2:
The patent introduces an intermediary mechanism (OER border process) that sits between conventional BGP routing and the optimization system. This intermediary collects routing information, exports it to the master process, and implements returned policies without disrupting existing BGP operations. This intermediary enables advanced optimization capabilities while maintaining compatibility with simple conventional routing protocols.
2Productivity
If comprehensive network traffic analysis is performed to optimize routing, then bandwidth utilization can be improved, but processing resources and time requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by having border processes continuously collect and export routing information to the master process before optimization is needed. The master process analyzes this pre-collected information and generates routing policies in advance. This preliminary data collection and analysis eliminates the need for time-consuming real-time analysis when routing decisions must be made, thus improving bandwidth utilization without excessive processing delays.
Solution Approach 2:
The patent extracts only the necessary routing information (prefixes, next-hop addresses, metrics) from complete network traffic data and exports it to the master process for analysis. This extraction of essential elements from comprehensive data reduces the volume of information that requires intensive processing, enabling efficient bandwidth optimization without the computational burden of analyzing all raw network traffic.
3Productivity
If routing optimization is implemented at all network nodes, then network traffic can be optimized comprehensively, but the system complexity and resource consumption increase
Solution Approach 1:
The system segments the routing optimization function into border processes (running on border routers) and a master process (running on a separate system). Border processes handle local routing information collection and policy implementation, while the master process handles centralized analysis and policy generation. This segmentation distributes complexity appropriately, enabling comprehensive network traffic optimization without requiring every network node to implement the full optimization system.
Solution Approach 2:
The OER border process serves multiple functions: it collects routing information from local BGP sessions, exports this information to the master process, and implements routing policies returned by the master. This multi-functional border process enables comprehensive optimization across the network while concentrating the complex analysis functions at the master level, reducing overall system complexity.
Data Source
AI summary
An Optimized Edge Routing (OER) technique provides efficiently data routing at the edge of a network or subnetwork. The technique employs a Master node that manages a set of border nodes located at the edge of the network or subnetwork. The Master node may be a stand-alone network management node or may be incorporated into a network node, such as a border node. Unlike prior implementations, the Master node instructs the border nodes to dynamically acquire (“learn”) prefixes of incoming and outgoing data flows and to selectively filter a set of learned address prefixes whose corresponding data flows match a predetermined set of criteria. The criteria may be based on routing metrics other than, or in addition to, conventional cost-based or distance-based metrics. Further, the criteria may include a set of filtering parameters that may be reconfigured, e.g., by the Master node, from time to time. Using the learned prefixes filtered by the border nodes, the Master node can distribute network traffic and utilize network bandwidth more efficiently than conventionally done.