IP-Route Clustering for IPv6 Last-Mile Outage Detection
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Solution Overview
Problem
Existing systems are inadequate for detecting smaller-scale Internet outages, particularly in IPv6 environments, as active probing is inefficient due to less densely utilized IP addresses and frequent IP address reassignment, making it difficult to identify issues like misconfigured devices or local network failures.
Innovation Solution
A system utilizing IP-route clustering and tree data structures to generate a network model, enabling efficient detection and characterization of outages by identifying the lowest common ancestor node, which can detect and pinpoint the source of failures in both IPv4 and IPv6 environments, including last-mile outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If active probing is used to detect outages in IPv6 environments, then outage detection can be performed, but the process becomes inefficient due to less densely utilized IP addresses and frequent IP address reassignment
Solution Approach 1:
The patent segments the network into IP-route clusters grouped by common ancestor nodes, allowing outage detection to focus on specific segments rather than probing the entire IPv6 address space. This enables efficient detection by targeting only relevant network segments affected by potential outages.
Solution Approach 2:
The patent introduces a network appliance as an intermediary that receives routing information from network nodes and generates probe packets. This intermediary system coordinates detection activities, manages the probing process, and aggregates results without requiring direct probing of every IPv6 address, thereby improving efficiency.
2Reliability
If large-scale ISP outages are monitored, then outage detection is achievable, but smaller-scale neighborhood or route outages remain difficult to detect
Solution Approach 1:
The patent divides the network into hierarchical segments based on common ancestor nodes, enabling detection at multiple scales. By organizing network nodes into clusters and sub-clusters with different ancestor nodes, the system can detect outages at the neighborhood level (within a cluster) while maintaining awareness of larger-scale ISP outages (across multiple clusters).
Solution Approach 2:
The patent implements local quality by tailoring detection strategies to specific network segments. Each IP-route cluster can be monitored with appropriate granularity based on its size and characteristics, allowing efficient detection of small-scale outages in specific neighborhoods while maintaining overall network awareness.
3Adaptability or versatility
If IP address reassignment is frequent, then network flexibility is improved, but outage detection accuracy deteriorates due to changing network topology
Solution Approach 1:
The patent performs preliminary actions by establishing IP-route clusters and common ancestor node relationships before outages occur. The network appliance continuously maintains routing information and updates cluster memberships, so when an outage happens, the system already has the structural framework needed for rapid accurate detection without being confused by IP reassignments.
Solution Approach 2:
The patent implements dynamics by making the network model adaptive to changing conditions. The system continuously updates routing information from network nodes and dynamically reorganizes IP-route clusters as network topology changes, ensuring that outage detection remains accurate even when IP addresses are reassigned or network structure evolves.
Data Source
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AI summary
Techniques for internet last-mile outage detection are disclosed herein. The techniques include methods for monitoring, by a network appliance associated with a network, a plurality of network nodes, detecting, by the network appliance, that a network node of the plurality of network nodes in a last mile of the network has disconnected from the network, overlaying, by the network appliance, the network node over a network model for at least a portion of the network including the network node to generate a model overlay, and determining, by the network appliance, a last mile outage source associated with a disconnection of the network node by identifying a lowest common ancestor node of the network node from the model overlay. Systems and computer-readable media are also provided.