CNS Core: Large: Collaborative Research: Network Design Automation

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

When we use Office365, Amazon, or Facebook, we access a world of rich services via a network. Network reliability is paramount for business, science, and government because downtime affects productivity, economic output, and social communication. However, reliability is difficult to achieve in the face of component failure, manual configuration, and the stringent economics of the Information Technology (IT) industry. This project will take first steps towards creating a new field of inquiry, Network Design Automation (NDA). NDA seeks to create computer-aided design (CAD) tools that reduce design effort and increase reliability based on a scientific understanding of network structure. NDA is inspired by Electronic Design Automation (EDA), a $7 billion industry that underpins the $100 billion chip industry, and is a vibrant intellectual discipline in its own right. NDA will verify networks using formal methods but also broaden the agenda to debugging networked systems and to other specialized networks such as rural networks and content distribution networks. Specific new tasks include tying application performance to network problems using machine learning, understanding failure logs using Natural Language Processing, creating more controllable routers inspired by hardware boundary scan ideas, rural network design using constraint satisfaction, network scripting to lower the barriers for operators, and data mining to find configuration bugs without a specification. Networks have unique challenges including heterogeneity, management complexity, rate of evolution (new data centers and service rollouts), and high availability needs. Besides a consistent focus on using algorithm search, we will leverage data mining, machine learning, Natural Language Processing, and hardware test approaches. NDA will not merely reuse existing mechanisms, but will exploit domain-specific insights about the structure of modern networks to invent new and scalable approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.