Date of Award

2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Computer and Information Science

First Advisor

Rajeev Alur

Second Advisor

Boon Thau Loo

Abstract

Network configurations are notoriously hard to write and maintain correctly. It requiresexpertise about the domain to write, frequent and laborious updates, and sometimes formal proof to ensure the absence of certain mistakes. The problem becomes more challenging with the popularity of software-defined network(SDN) in recent years, which aims to give users more flexible control over the network’s dynamic behaviors. There has been research on automating the process of configuring the network. However, much of it requires users to learn a specific programming abstraction or interface. Since network operators are a group generally unfamiliar with programming, using these systems may go beyond their abilities. It is also hard to ensure these systems are scalable and accurate enough for real-world usecases. They mostly lack both design considerations to address scalability and accuracy, and also a systematic evaluation of the two metrics in practical scenarios. In this work, we propose a series of approaches to automate network programming. They are based on specifications that are easy and natural to obtain by network operators. We also apply novel program analysis techniques to speed up the process of finding a program that can accurately capture the intention of the specification. We have evaluated our systems on a broad range of benchmarks obtained from real-world data. They have shown ability to finish complex programming tasks within minutes and achieved very high accuracy.

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