Improving Stack-Wide Resource Utilization for a Faster Mobile Web

***Due to the developing situation surrounding the COVID-19 virus, this talk will be available for remote viewing.  See below for details.***

Abstract: Mobile web pages are integral to today's society, supporting critical services such as education, e-commerce, and social networking. Despite considerable academic and industrial research efforts, and major improvements over the past decade across the client-side web stack (i.e., networks, device CPUs, and browser engines), page load performance has plateaued and continues to fall short of user performance demands in practice. The consequences of this are far reaching: users abandon pages early, costing content providers billions of dollars in lost revenue; or pages are unusably slow, particularly in developing regions where web pages are often the sole gateway to the aforementioned services.

In this talk, I will describe the origin of this performance plateau in the context of serialized page load tasks that preclude effective utilization of the underlying network and CPU resources. Then, I will describe two complementary optimizations that my students and I have developed to eliminate these inefficiencies throughout the page load process and cut mobile load times in half. Key to these optimizations are judicious applications of programming languages (e.g., symbolic execution) and machine learning (e.g., reinforcement learning) techniques that enable us to 1) discover optimization knobs that preserve application correctness, and 2) tune those knobs according to stack-wide signals from the network, device, page, and browser, without developer intervention. I will conclude by describing how these underlying techniques can motivate and address a range of future challenges in networked applications and distributed systems. 

Bio: Ravi Netravali is an Assistant Professor of Computer Science at UCLA. His research interests are broadly in computer systems and networking, with a recent focus on building practical systems to improve the performance and debugging of large-scale, distributed applications for both end users and developers. His research has been recognized with an NSF CAREER Award, a Google Faculty Research Award, an ACM SoCC Best Paper Award, and an IRTF Applied Networking Research Prize. Prior to joining UCLA, Netravali received a PhD in Computer Science from MIT in 2018.

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Zoom information:
Topic: Ravi Netravali CS Seminar
Time: Mar 23, 2020 12:00 PM Eastern Time (US and Canada)

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