Daniel Suo will present his General Exam Presentation TODAY May 25, 2018 at 10:00 am in CS 233. Committee Kai Li (Adviser) Jennifer Rexford Brian Kernighan Title: Deadlines for streaming machine learning applications Abstract: Within the last decade, advances in machine learning have created opportunities for streaming systems to act more effectively on incoming data. However, the typical metrics system designers optimize—throughput and latency—are not sufficient for a large, diverse class of machine learning applications (e.g., real-time neurofeedback., autonomous driving, fusion energy reactors) that demand highly predictable latency, or deadlines. These important applications, not possible before the recent developments in machine learning, face three unattractive options for meeting their deadlines: 1) rely on hand-tuned and job-specific allocation and scheduling, 2) limit their computational model to use traditional real-time algorithms and restrict many of the critical machine learning methods, or 3) suffer priority inversions and unnecessary overhead with low-latency systems. Their fourth option—missing deadlines—can result in catastrophic loss of life and property. In this talk, we make the case for a stream processing system that explicitly defines and reasons about deadlines and discuss some of our early work in the area. In particular, we describe techniques for efficiently identifying and remediating late tasks as early as possible. We show some illustrative results that suggest that the standard low-latency approach meets deadlines less reliably (by up to one third) or consumes more resources (by a factor of 1.5x) than a deadline-centric approach. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University