Experiences with Modeling Network Topologies at Multiple Levels of Abstraction
Abstract
Network management is becoming increasingly automated,
and automation depends on detailed, explicit representations
of data about both 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.
We describe MALT, a Multi-Abstraction-Layer Topology
representation, which supports virtually all of our network
management phases: design, deployment, configuration, operation, measurement, and analysis. MALT provides interoperability across software systems, and its support for abstraction allows us to explicitly tie low-level network elements to high-level design intent. MALT supports a declarative style that simplifies 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.
and automation depends on detailed, explicit representations
of data about both 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.
We describe MALT, a Multi-Abstraction-Layer Topology
representation, which supports virtually all of our network
management phases: design, deployment, configuration, operation, measurement, and analysis. MALT provides interoperability across software systems, and its support for abstraction allows us to explicitly tie low-level network elements to high-level design intent. MALT supports a declarative style that simplifies 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.
Research Areas
