Lune

NSDI2020顶会

Experiences with Modeling Network Topologies at Multiple Levels of Abstraction

Jeffrey C. Mogul, Drago Goricanec, Martin Pool, Anees Shaikh, Douglas Turk, Bikash Koley, Xiaoxue Zhao

出版方
2020年份
43被引次数
17顶会引用

摘要

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.

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

lune papers fulltext e55d5ad7-4151-4b9d-a78a-e9e24f996e8d

引用它的顶会 Paper17

问问它们各自怎么用它

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖