Experiences with Modeling Network Topologies at Multiple Levels of Abstraction

Authors: 

Jeffrey C. Mogul, Drago Goricanec, Martin Pool, Anees Shaikh, Douglas Turk, and Bikash Koley, Google; Xiaoxue Zhao, Alibaba Group Inc.

Abstract: 

Network management is becoming increasingly automated, and automation depends on detailed, explicit representations of data about the state of a network and about an operator's intent for its networks. In particular, we must explicitly represent the desired and actual topology of a network. Almost all other network-management data either derives from its topology, constrains how to use a topology, or associates resources (e.g., addresses) with specific places in a topology.

MALT, a Multi-Abstraction-Layer Topology representation, supports virtually all network management phases: design, deployment, configuration, operation, measurement, and analysis. MALT provides interoperability across our network-management software, and its support for abstraction allows us to explicitly tie low-level network elements to high-level design intent. MALT supports a declarative style, simplifying what-if analysis and testbed support.

We also describe the software base that supports efficient use of MALT, as well as numerous, sometimes painful lessons we have learned about curating the taxonomy for a comprehensive, and evolving, representation for topology.

NSDI '20 Open Access Sponsored by NetApp

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BibTeX
@inproceedings {246334,
author = {Jeffrey C. Mogul and Drago Goricanec and Martin Pool and Anees Shaikh and Douglas Turk and Bikash Koley and Xiaoxue Zhao},
title = {Experiences with Modeling Network Topologies at Multiple Levels of Abstraction },
booktitle = {17th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 20)},
year = {2020},
isbn = {978-1-939133-13-7},
address = {Santa Clara, CA},
pages = {403--418},
url = {https://www.usenix.org/conference/nsdi20/presentation/mogul},
publisher = {{USENIX} Association},
month = feb,
}

Presentation Video