Fast, Flexible, and Intelligent Next-Generation Networks and Systems
For so long, the continuous growth in chips’ processing capacity has helped preserve the end-to-end principle, keeping central-processing units (CPUs) general-purpose and smart while having network switches fixed-function and dumb, to process and forward information at speed. Since the turn of the century, two major trends are now challenging this principle, forcing us to rethink how we build CPUs and switches and the role they play: (a) the decline of Moore’s Law and (b) the rise of big data (e.g., social media, video streaming, augmented/virtual reality, internet of things, and artificial intelligence)—generating quintillion bytes of information every day. The chip area and energy budget are not increasing anymore; CPUs must now repurpose resources (like caches, out-of-order execution, and more) needed for general-purpose processing to logic tailored to a particular application domain (e.g., machine learning or serverless compute). Likewise, switches must now take on more responsibilities (e.g., congestion control and packet scheduling) to further offload CPUs; they can no longer be fixed-function, line-rate devices for packet forwarding only. The concern, however, is that doing so will cause systems to sacrifice flexibility for higher performance, and networks to sacrifice performance for more flexibility. In this talk, I will discuss some of the networking and systems architectures we have developed to address this ever-rising tension between performance and flexibility.
Muhammad Shahbaz, Purdue University
Muhammad Shahbaz is a Kevin C. and Suzanne L. Kahn New Frontiers Assistant Professor in Computer Science at Purdue University. His research focuses on the design and development of domain-specific abstractions, compilers, and architectures for emerging workloads (including machine learning and self-driving networks). Shahbaz received his Ph.D. and M.A. in Computer Science from Princeton University and B.E. in Computer Engineering from the National University of Sciences and Technology (NUST). Before joining Purdue, Shahbaz worked as a postdoc at Stanford University and a Research Assistant at Georgia Tech and the University of Cambridge. Shahbaz has built open-source systems, including Pisces, SDX, and NetFPGA-10G, that are widely used in industry and academia. He received the Facebook and Intel Research Awards; IETF/IRTF ANRP Prize, ACM SOSR Systems Award; APNet Best Paper Award; Best of CAL Paper Award; and Internet2 Innovation Award.