Description:

Monday / CS Seminar
December 2nd / 12:00 PM
Hybrid / Mudd 3514

Speaker
Izzy Grosof, Northwestern University

Talk Title
Multiserver Stochastic Scheduling for Large-Scale Computing

Abstract

 Large-scale computing systems are massively important, using over 1% of the world's electricity. It is vital that we can forecast the performance of these systems, and control the systems to be both fast and resource-efficient. Stochastic modeling and scheduling theory are key tools towards that goal. Prior scheduling theory is not equipped to handle large multiserver systems, with little known about modeling the performance of such systems, much less how to optimally control such systems.

I'll cover my recent results in two important multiserver models. First, the M/G/k, a model where each jobs requires the same amount of resources. Next, the multiserver-job model, a model where different jobs require different amounts and kinds of resources.

In each model, I'll present theoretically-motivated scheduling policies which are practical to apply to real systems, from datacenters to microservices architectures. Our results include optimal performance in the limit as system load approaches capacity, as well as throughput optimality and performance analysis.

Biography
Izzy Grosof is a new assistant professor faculty in the IEMS department here at Northwestern. Their research focuses on stochastic queueing, resource allocation, optimal scheduling, and performance analysis. They received their PhD in CS from Carnegie Mellon in 2023, and subsequently completed postdocs at Georgia Tech and UIUC. Their research received the Best Student Paper, Best Paper, and Best Dissertation awards from the INFORMS, SIGMETRICS, and Performance conferences.

Research/Interest Areas: Stochastic queueing theory for large-scale computing, emphasizing scheduling theory
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Zoom: https://northwestern.zoom.us/j/92919202717?pwd=eMIa3miqyHV7KnbzFKEhbYU39sB35V.1
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DEI Minute: tinyurl.com/cspac-dei-minute