[Colloquium] Re: [asst-p] [fac] CS Seminar Apr. 12: Alexey Tumanov, UC Berkeley

[Sanjay Krishnan]

Hey all,
Just a reminder that this is today at 11 am (not our usual slot)!

Sanjay

On Thu, Apr 4, 2019 at 6:54 PM Sandra Wallace <swallace at cs.uchicago.edu>
wrote:

> UNIVERSITY OF CHICAGO
> DEPARTMENT OF COMPUTER SCIENCE
> PRESENTS
>
>
>
> *Alexey Tumanov*
>
> *UC Berkeley*
>
>
> *Friday, April 12, 2019 at 11:00 amCrerar 390*
>
>
> *Title:  **Resource Management and Scheduling for Emerging AI
> Applications*
>
> *Abstract:*
> A new class of Artificial Intelligence applications is emerging that
> imposes a challenging set of requirements on how we program the cloud and
> how we manage cloud resources efficiently. With the end of Moore’s Law and
> Dennard scaling, coupled with simultaneous increase in the heterogeneity of
> increasingly interactive AI applications with end-to-end latency
> constraints, the future of AI Systems depends on advances in resource
> management and scheduling for these applications. First, these applications
> generate an increasingly heterogeneous set of tasks, both in the resources
> optimal for their performance and in the time scale of individual tasks.
> Second, they are increasingly user-facing, imposing a set of soft real-time
> constraints on the frameworks serving these workloads. Third, they
> individually expect or benefit from heterogeneous and often conflicting
> resource allocation policies --- a challenge for unifying frameworks that
> aim to support them. Thus, a set of three emergent requirements must be
> efficiently addressed: (1) heterogeneity awareness in space and time, (2)
> soft real-time end-to-end latency constraints, and (3) scheduling policy
> heterogeneity at the application level. To address these requirements, I
> will present (1) TetriSched --- a mathematical framework to capture the
> performance as a function of resource space and timeliness requirements of
> these applications for cost-efficient and heterogeneity-aware resource
> allocation, (2) Inferline --- a soft real-time system for achieving these
> requirements under unpredictable bursty workloads when multiple ML models
> are composed for inference; (3) Ray --- an active open source project that
> brings some of these ideas together and serves as the unifying framework
> for distributed ML, addressing the challenge of scheduling policy
> heterogeneity.
>
>
> *Bio:*
> *Alexey Tumanov is a Postdoctoral Researcher at the University of
> California Berkeley, working with Ion Stoica and collaborating closely with
> Joseph Gonzalez in RISELab, Department of Computer Science. Alexey
> completed his Ph.D. at Carnegie Mellon University, advised by Gregory
> Ganger. At Carnegie Mellon, Alexey was awarded the prestigious Canadian
> government fellowship, NSERC Alexander Graham Bell Canada Graduate
> Scholarship (NSERC CGS-D3) and was a member of the Intel Science and
> Technology Center for Cloud Computing and the Parallel Data Lab. Alexey’s
> Systems research spanned the entire stack, starting with agile stateful VM
> replication with para-virtualization at the University of Toronto (working
> with Eyal de Lara) and most recently involving resource management for
> emerging AI applications. Alexey is the recipient of several awards,
> including the Best Graduate Student Teaching Assistant at Carnegie Mellon
> and the Best Student Paper award for his thesis work on TetriSched at
> EuroSys 2016.*
>
> *Host:  Sanjay Krishnan*
>
> *PDF:*
>
>
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