Complexity evaluation of network configurations and abstractions

TL;DR

This paper introduces a graph-based framework to objectively compare various network abstraction models, revealing differences in complexity and efficiency that impact their industry adoption.

Contribution

It proposes a novel comparison framework using graph metrics to evaluate and contrast network abstractions' simplicity, efficiency, and effectiveness.

Findings

Some abstractions like Kubernetes are efficient for storing policies.

Public cloud models are more infrastructure-centric and complex.

The framework helps guide the development of more effective network abstractions.

Abstract

Computer networks have been traditionally configured by humans using command-line interfaces. Some network abstractions have emerged in the last 10 years, but there is no easy way of comparing them to each other objectively. Therefore, there is no consensus in the industry of what direction modern network abstractions should take, and the adoption of these abstractions lags as a consequence. In this paper I propose a comparison framework using metrics derived from graph structures to evaluate the simplicity, efficiency, and effectiveness of different network abstraction models. The result of this comparison is that while some of the existing network abstractions are quite efficient to store network policy (such as the Kubernetes or the Cisco Application Centric Infrastructure models), others (notably public cloud) are still very infrastructure-centric and suffer from excessive complexity.