CAREER: Large-scale Dynamic Reconfigurable Networks

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Due to society’s ever-increasing dependency on online services, reliable working of the underlying communication networks is of paramount importance. For modern online services, emerging workloads (such as remote video calls, augmented reality, machine learning, and health care) depend highly on the underlying network’s response. However, the design of today’s network infrastructures still treats the physical layer of networks as a static black box with minimal reconfigurability. This project seeks to develop new paradigms for large-scale dynamic reconfigurable networks that are applicable to datacenter networks and software-defined private wide area networks to improve service delivery. The core mission of the project is to make physical layer reconfigurability an intrinsic part of future networks. The project focuses on high-impact use-cases and applications to develop novel solutions for reconfigurable networks by leveraging optical technologies. To make large-scale reconfigurable networks a reality, this proposal tackles the foundational challenges of high-performance reconfigurable systems, including: (1) A set of algorithmic and system design techniques to co-optimize the network topology jointly with the parallelization strategy of emerging distributed machine learning jobs in datacenter networks. (2) A set of optimization and learning-based techniques to build practical cross-layer solutions for reconfigurable software-defined private wide-area networks while providing guaranteed performance. (3) Techniques to balance algorithmic and engineering foundations for reconfigurable systems. Deploying reconfigurable networks will enable users around the world to have access to reliable and fast online services. As a result, this project has the potential of high industry impact. From an educational perspective, the project will develop a new graduate-level course on Systems for Machine Learning and Machine Learning for Systems. This emerging area at the intersection of machine learning and optical systems is driven by the explosive growth of diverse applications of artificial intelligence and the complexity of large-scale systems. This project will develop a variety of simulated and emulated environments with a focus on machine learning workloads and techniques which will be accessible to a large community of students and researchers who may not have expertise in these areas. The data generated through the work in this project will consist of papers, source code, and benchmarks and will be released at the following website: http://reconfignets.csail.mit.edu/ Data will be retained for at least three years beyond the award period. 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.