Internet-Draft Mangrove: A Unified Framework for Intern March 2024
Yang, et al. Expires 5 September 2024 [Page]
Intended Status:
Standards Track
Y. R. Yang
Yale University
B. L. Lewis
Yale University
D. M. Mertus
Yale University
A. S. Shi
Yale University
J. Z. Zhang
Yale University

Mangrove: A Unified Framework for Internet Topology, Routing Abstraction, and Modeling


This document describes a novel system called Mangrove for obtaining global visibility across the Internet. It achieves this through constructing a comprehensive, unified representation of the Internet's topology and routing behavior by aggregating and indexing diverse data sources. he core challenge is obtaining an accurate, up-to-date model of how packets traverse the global Internet given the Internet's vast scale, dynamic nature, and fragmented visibility across multiple organizations collecting data in different formats.

Mangrove addresses this by leveraging the hierarchical structure and natural abstractions of Internet architecture. It collects and integrates data from traceroute measurements, BGP routing tables, IP allocation records, and active broadband measurements. This multi-source data is partitioned and indexed across multiple granularity levels - geographic regions, autonomous systems, and IP prefixes - allowing effective storage, querying, and scalable processing.

Mangrove constructs a unified topology and routing representation augmented with an extensible ruleset derived from Internet axioms and measured data. For arbitrary source-destination pairs, it computes estimated packet paths and associated performance metrics at the highest feasible granularity level based on available information completeness. The system handles real-time Internet dynamics through incremental rule refinement using detected changes in routing data and measurements. This enables timely updates to the topology model without full recomputation. Mangrove aims to provide researchers and operators an unprecedented unified Internet view for analysis, optimization, planning, security, and regulatory tasks.

Status of This Memo

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Table of Contents

1. Introduction and Motivation

The Internet is a vast and complex network of interconnected devices, spanning the globe and enabling communication between billions of users and systems. Understanding the underlying topology and routing model of the Internet is crucial for researchers, network operators, and service providers to gain insights into routing dynamics, performance characteristics, and potential vulnerabilities. However, the current state of Internet topology and routing mapping is fragmented, with multiple organizations and projects collecting data through various measurement techniques, such as traceroutes, BGP looking glass dumps, and performance metrics. This data is often inconsistent in format, incomplete, and distributed across multiple sources, making it challenging to obtain a comprehensive and unified view of the Internet's topology. We wish to build a system that unifies this information into a single representation of routing and topology models of the Internet at different points in time, allowing for queries to be performed against this representation.

By creating a comprehensive model of the Internet’s topology and routing dynamics, this system will enable a wide range of applications and analysis, including:

  1. Network monitoring/troubleshooting: The ability to detect and localize routing anomalies, such as loops, black holes, or performance degradation can aid in identifying and resolving network issues more efficiently.

  2. Vulnerability assessment and security analysis: A unified view of the Internet's topology and routing can help identify potential vulnerabilities, such as single points of failure or routing hijacks, enabling proactive measures to enhance network security and resilience.

  3. Traffic Engineering and Capacity Planning: By understanding the flow of traffic across the Internet, service providers can optimize resource allocation, load balancing, and capacity planning, improving overall network performance and efficiency.

  4. Resilience and Disaster Recovery Planning: Modeling the impact of potential outages or failures on the Internet's topology and routing can inform disaster recovery strategies and contingency plans, ensuring business continuity and minimizing service disruptions.

  5. Privacy and Regulatory Compliance Analysis: The ability to map data flows across the Internet can aid in identifying potential privacy violations or regulatory non-compliance, enabling organizations to take proactive measures to protect user data and comply with relevant regulations.

2. Problem Statement

Being able to construct a digital twin of a network as large as the Internet comes with unique requirements for our system. These are, in no particular order:

  1. Scalability: The sheer scale of the Internet poses a significant challenge for our system. With over 17 billion devices connected and more than 60,000 networks or Autonomous Systems (ASes), each with its own internal topology, storing information about the paths interconnecting all these devices requires us to employ efficient indexing mechanisms and data structures that enable fast queries over large repositories of data.

  2. Diverse Data Sources: The data required for topology mapping is scattered across multiple organizations, each with their own data collection methodologies, formats, and access methods (discussed below). The diversity of formats, from traceroute data to BGP table dumps, coupled with varying updating cadences and access methods, presents a significant challenge. Furthermore, the quality and completeness of the data can vary based on the vantage points of the collectors, leading to potential gaps or inconsistencies. Our system must be capable of integrating data from these diverse sources in a way that is consistent and unified.

  3. Partial Information: Many topology measurement techniques, such as traceroutes, can produce incomplete or missing information due to factors like firewalls, packet filters, or unresponsive devices. This is often manifested as missing hops represented by asterisks (*) in the traceroute data. Additionally, BGP routing data lacks granularity below the Autonomous System (AS) level paths. IP geo-mapping databases, used to associate IP addresses with physical locations, also have inherent inaccuracies. Furthermore, performance data may be sampled or estimated statistically rather than being comprehensive. As such, our system must be capable of producing the best-effort topology and routing representation for packets between two hosts, even when the input data is incomplete or partially missing.

  4. Real-time, Dynamic Updates: The Internet is a highly dynamic environment, with routing changes, network outages, and topology shifts occurring continuously. Events like outages, attacks, or policy changes can trigger changes in the routing behavior, with BGP convergence times ranging from seconds to minutes after an event. New links and nodes are constantly being added, while others are going down. Our system needs mechanisms to detect these relevant changes and incrementally update the topology representation in real-time or near real-time. However, this requirement presents a trade-off between the cost of performing full recomputations versus maintaining and applying deltas or incremental updates. Additionally, our system must meet the time window requirements for how current the topology view needs to be, depending on the specific use case or application.

3. Internet Structure Overview

At a high level, the objective of our framework is to compute a digital twin of the Internet routing system, and this system is complex:

The Internet is composed of several layers, each serving a distinct function:

  1. Link Layer: Governs the transfer of data between directly connected nodes.

  2. Internet Layer: Handles logical addressing and routing of data across the network.

  3. Transport Layer: Manages end-to-end communication and ensures reliable data transfer.

  4. Application Layer: Enables network services and applications to interface with the underlying transport protocols.

Sitting on top of these layers, Autonomous Systems (ASes) and the Border Gateway Protocol (BGP) govern how packets traverse networks operated by different administrative entities.

The behavior of the Internet can be characterized by two principal components: the underlying network topology and the routing policies enforced by the protocol ruleset. Network topology delineates the physical connectivity constraints that determine the paths packets can traverse. However, network infrastructure is in a constant state of flux as devices are continually added, removed, or reconfigured, posing challenges for comprehensive topology measurement outside an AS's purview. Routing policies dictate how packets are forwarded along available paths in accordance with protocol rules. Protocols like Equal-Cost Multi-Path (ECMP) routing introduce complexity, making the determination of single best paths more difficult. Furthermore, most devices do not expose their Forwarding Information Bases (FIBs), obscuring the precise ruleset implementations.

Consequently, measurements provide the primary evidence of the effects induced by the combination of the underlying topology and the applied routing policies. This abstraction of rule systems onto physical network elements results in observable path properties, such as latency, bandwidth, and jitter, which are crucial for characterizing end-to-end packet delivery performance between nodes.

4. Existing Systems Overview

Current solutions for internet visibility topology generally divide their focuses across two key categories: topology and routing inference and metadata measurement. Additionally, these solutions further subdivide into consumer solutions and enterprise solutions providing a mix of both aggregate and granular data measured in varying units of time.

Topology and routing measurement solutions employ a collection paradigm that includes:

  1. Distributed Nodes: Operated either by the organization itself or external contributors, these nodes form the backbone of data collection efforts.

  2. Periodic Traceroute Measurements: Using tools such as paris traceroute, measurements are periodically collected between nodes to infer network topology.

  3. Data Inference: The collected data is analyzed to infer the network topology, relying on the information returned by traceroute measurements.

Metadata measurement solutions diverge from topology measurement primarily in the nature of data collected. Unlike topology measurement, which focuses on the structure of the network, metadata measurement seeks to understand the properties of network paths. This requires a significantly larger dataset, typically sourced from a wide user base employing network measurement tools. The process involves:

  1. Connection to a Local Server: Establishing a baseline for measurement by connecting to a server in close proximity to the user.

  2. Connection Saturation: Maximizing the use of the connection to ensure comprehensive data collection.

  3. Measurement Execution: Performing measurements to gather data on the connection.

  4. Optional Traceroute Measurement: If feasible, supplementing the measurement with traceroute data for enhanced insight.

  5. Statistical Inference: Applying statistical methods to the collected data to extrapolate insights on broadband measurements, with a focus on geographical specificity.

5. Mangrove: A Framework For Internet Abstraction and Modeling

The existing choice between focusing on traceroute data or broadband connection measurements presents a significant trade-off for organizations. To generate an accurate topology, organizations need control: owning the network devices, collecting traceroute data on a consistent schedule, and sending as many traceroutes as possible in ways that maximize inference. However, broadband measurement requires detail and quantity: distributing access nodes, collecting as much consumer information as possible, using traceroutes as only secondary sources of information. This tradeoff in required infrastructure has made it difficult to bridge the gap between topology, path inference, and associated metadata.

Topology and routing inference is done through the analysis of data retrieved from techniques including traceroute experiments and BGP Looking Glass dumps. This raw data alone provides insight into the ‘real’ structure of the Internet; traceroute measurements form the backbone of most existing Internet topology mapping efforts, and when combined with Autonomous System routing data, can be used to infer the paths that exist throughout the internet. The primary technique for collecting these involves deploying a distributed set of vantage points or measurement nodes across the Internet. These nodes periodically initiate traceroute experiments towards predetermined destination IP addresses or prefixes. Many topology mapping projects like CAIDA's Ark and RIPE Atlas have deployed large measurement infrastructures consisting of hundreds or thousands of vantage points globally. These provide diverse perspectives into Internet paths from different edge networks.

Most existing platforms focus on either traceroute data collection and analysis, or BGP/routing data. Very few solutions currently attempt an integrated data fusion across measurements, routing, geolocation and performance data.

We propose the engineering of a scalable system that aggregates and indexes data from various sources to construct a single, unified representation of Internet topology. This representation would provide researchers and network operators with rich information about the connectivity between devices on the Internet, augmented by performance metrics that could inform them about connection quality between networks.

5.1. Methodology

To construct this unified representation, our system will leverage the natural abstractions offered by Internet architecture to provide the best level of estimates for topologies and routing decisions. The core methodology involves the following steps:

  1. Data Aggregation: Collect and integrate data from multiple sources, including CAIDA Ark traceroute measurements, BGP looking glass dumps, Regional Internet Registry (RIR) records, and performance data from projects like M-Lab, Ookla, and perfSONAR.

  2. Abstraction: Leverage the natural abstractions in Internet architecture, such as the Autonomous System (AS) level, to represent topology at different granularities. This allows the system to handle incomplete data and provide estimates at varying resolutions. Represent this as a multidimensional graph where nodes can aggregate, inherit, or point to other nodes on the graph depending on the abstraction layer.

  3. Rule Generation: Use network axioms to generate an initial set of rules for AS pathing using BGP dumps, traceroute first hops, and business relationships. The system then repeatedly generates and refines lower rule sets using axioms, AS pathing, FIBs, and known clustering policies for levels of resolution from subnet to devices.

  4. Compression: Completed rules are grouped into sets for their particular header space. The system stores such classes in a tree which represents IP prefixes and addresses. Indexing into and moving down levels for a chosen destination determines the rule-set. This allows for constant-time indexing, with a slight space tradeoff.

  5. Path Inference: For a given source and destination IP address pair, the system will attempt to provide the highest certainty estimate of the path a packet would take, based on the aggregated data and rule trie. If complete traceroute data is available, it will be used to provide a detailed IP-level path. If not, the system will attempt to construct a path using the rule set for its IP prefix and graph edge weights. The final fall back is to AS-level paths derived from BGP routing information.

  6. Performance Enrichment: Augment the topology representation with performance metrics, such as bandwidth, latency, and throughput, obtained from sources like M-Lab and Ookla. Such data can be added by matching consumer collected traceroutes with those of topology measurement solutions. For AS-level paths, performance metrics can be averaged across nodes within each AS.

With the above, we will provide users with an interface that they may use to query the path that a packet would take between any two arbitrary source and destination IP addresses. Our system will leverage all of the information it has aggregated to provide a ‘best estimate’ of this path, while augmenting this with performance metrics at the highest level of resolution.

To illustrate this, we consider a sample user query where a user wishes to know the path a packet would take between source IP address A and destination IP address B.

At a high level, our system will do the following:

  1. Map each IP address to their corresponding AS numbers: the system will maintain an IP address to AS lookup as a result of aggregating this data from several sources over time. The most recent match for the IP address will be used.

  2. Analyze BGP routing data for each corresponding AS: if the IP addresses lie in separate autonomous systems, we may query each of the corresponding BGP table representations for each AS to determine the AS path that a packet will use to get from A to B.

  3. Infer AS-level Path: After determining the AS numbers for the source and destination IP addresses, and analyzing the BGP routing data for the corresponding ASes, the system can infer the AS-level path that a packet would take from the source AS to the destination AS. This AS-level path would be a sequence of AS numbers, representing the path through different Autonomous Systems.

  4. Retrieve Traceroute Data for Each AS Hop: For each AS hop in the inferred AS-level path, the system will attempt to retrieve relevant traceroute data to reconstruct the IP-level path within that AS. This can be done by querying the indexed traceroute data based on the source and destination AS numbers for that particular hop.

  5. Reconstruct IP-level Path Within Each AS: If traceroute data is available for an AS hop, the system will use that data to reconstruct the IP-level path within that AS.If no traceroute data is available, the system will treat that AS hop as a single hop in the final path, representing the AS-level transition.

  6. Handle Missing Hops and Incomplete Paths: If there are missing hops (represented by *) within an AS hop, the system will group consecutive * hops and represent them as a single AS-level hop in the final path. If no traceroute data is available for an entire AS hop, the system will represent that hop at the AS-level in the final path. This illustrates our concept of ‘granularity’ - where there is missing data at a specific resolution, we may resort to using data at a lower resolution (the AS level) to still provide some insight into the path.

  7. Associate Performance Metrics: The system can then associate relevant performance metrics, such as latency, bandwidth, or throughput, with each hop in the final path. In cases where our system must resort to lower resolutions, we may use aggregation and averaging techniques to provide a rough estimate on connectivity statistics for that portion of the path.

5.2. System Architecture

The proposed system will consist of the following key components:

  1. Data Collectors: Modules responsible for fetching and processing data from various sources, such as CAIDA Ark, BGP looking glass servers, RIRs, and performance measurement platforms.

  2. Data Unification Engine: A central component that integrates and indexes the collected data into a unified representation, handling data format conversions, deduplication, and consistency checks.

  3. Topology Database: A scalable and distributed data store designed to store and query the unified topology representation, supporting both IP-level and AS-level granularities. The data will be represented as a multi-dimensional graph, its associated rule sets in equivalence classes, and a compressed trie.

  4. Query Engine: An engine with a custom query language that can quickly join data at multiple levels of resolution depending on the user request.

  5. Interface: An API or user interface that allows researchers and network operators to submit source-destination IP pairs and retrieve the corresponding estimated paths, along with associated performance metrics.

  6. Real-time Update Handler: A component that monitors for changes in the underlying data sources and triggers updates to the topology representation, ensuring it remains current with the dynamic nature of the Internet.

5.3. Scalability

With regards to discussion about scalabiltiy, the partitioning and distribution of data across different levels can be structured as follows:

5.3.1. Geographic

At the geographic level, the system can partition the data based on geographic regions or clusters. This level is responsible for storing and managing the BGP routing data for the Autonomous Systems (ASes) within each geographic cluster. The primary function of this level is to handle AS-level path inference and resolution based on the BGP routing data.

5.3.2. AS

Within each geographic partition, the system can further partition the data by individual AS. At this level, each AS is responsible for storing and managing its own internal routing topology and traceroute data. This level is designed to handle IP-level path reconstruction and inference within the boundaries of a single AS.

The AS-level partitions can maintain the following data:

  1. Internal Routing Topology: This includes the router-level topology within the AS, representing the interconnections between routers and network devices.

  2. Traceroute Data: Traceroute measurements conducted within the AS, capturing the IP-level paths between different ingress and egress points or subnets.

  3. Performance Metrics: Performance data (e.g., latency, bandwidth) associated with links or paths within the AS, obtained from sources like M-Lab or Ookla.

5.3.3. Subnet/Prefix

To further enhance granularity and scalability, the system can introduce an additional level of partitioning at the subnet or IP prefix level. This level can store traceroute data and performance metrics specific to individual subnets or IP prefixes within an AS.

By partitioning the data at these multiple levels, the system can distribute the workload and storage requirements across different components, allowing for parallel processing and improved scalability.

The workflow for resolving a path between a source and destination IP address would involve the following steps:

  1. The geographic partition responsible for the source and destination ASes is identified, and the AS-level path is determined using the BGP routing data.

  2. For each AS hop in the inferred AS-level path, the corresponding AS partition is queried to retrieve traceroute data and reconstruct the IP-level path within that AS.

  3. If further granularity is required or available, the subnet/prefix level partitions can be consulted to obtain more detailed traceroute data and performance metrics for specific IP prefixes or subnets along the path.

6. IANA Considerations

This document has no actions for IANA.

7. Security Considerations

The collection, distribution of MoWIE information should consider the security requirements on information privacy and information integration protection and authentication in both sides. Since the network status is not directly related to any special user, there is currently no any privacy issue. But the information transmitted to the application can pass through a lot of middle box and can be changed by the man in the middle. To protect the network information, an end to end encryption and integration is needed. Also, the network needs to authenticate the information exposure provided to right applications. These security requirements can be implemented by the TLS and other security mechanisms.

Authors' Addresses

Y. Richard Yang
Yale University
Watson 208A, 51 Prospect Street
New Haven, CT 06511
United States of America
Bradley Lewis
Yale University
33 Lynwood Pl
New Haven, CT 06511
United States of America
Daniel Mertus
Yale University
Unit 404, 367 Elm Street
New Haven, CT 06511
United States of America
Alex Shi
Yale University
33 Lynwood Pl
New Haven, CT 06511
United States of America
Joseph Zhang
Yale University
33 Lynwood Pl
New Haven, CT 06511
United States of America