Self-Programming Networks: Applications to Financial Trading Systems
Abstract
We describe Self-Programming Networks (SPNs), an ongoing research effort at Stanford for making cloud computing networks autonomous; that is, to enable the networks to sense and monitor themselves, and program and control themselves. We describe the goals and the architecture of SPNs and present two key outcomes: (i) Huygens, for scalable and accurate clock synchronization, and (ii) Simon, for fine-grained network telemetry using observations from the network’s edge. We also describe the relevance of this work to existing financial trading systems and demonstrate how, in future, it enables financial trading systems in the Cloud.
Bio
Balaji Prabhakar is VMware Founders Professor of Computer Science, Electrical Engineering and, by courtesy, in the Graduate School of Business, Stanford University. Balaji’s research interests are in computer networks; notably, data centers and cloud computing. From 2008 to 2016 he worked on Societal Networks: networks vital for society’s functioning, such as transportation, electricity and recycling systems. He led the development and deployment of “nudge engines” for transportation systems (notably, Singapore mass transit and the Bay Area Rapid Transit (BART)), wellness programs, and corporate learning programs. Based on this work, he co-founded Urban Engines, a startup which was acquired by Google in 2016.