Harikaran Subbaraj

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2020-75
May 28, 2020

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-75.pdf

The need for systems to support machine learning inference has grown as the im- portance of machine learning in production systems has increased. Serving pipelines of machine learning models comes with challenges of scaling, low-latency require- ments for requests, and high computation costs for different stages of the pipeline. In this paper we propose that by taking a dataflow abstraction we can simplify and increase performance of serving these machine learning pipelines. The proposed system FLOWSERVEcombines this dataflow paradigm with Cloudburst, a stateful function as a service (FaaS) system to provide a framework to deploy and serve machine learning pipelines at scale. We provide several logical and physical opti- mizations that make FLOWSERVEoutperform currently used research and industry systems.

Advisor: Joseph Gonzalez

BibTeX citation:

@mastersthesis{Subbaraj:EECS-2020-75,
    Author = {Subbaraj, Harikaran},
    Title = {Using Dataflow for Machine Learning Inference},
    School = {EECS Department, University of California, Berkeley},
    Year = {2020},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-75.html},
    Number = {UCB/EECS-2020-75},
    Abstract = {The need for systems to support machine learning inference has grown as the im- portance of machine learning in production systems has increased. Serving pipelines of machine learning models comes with challenges of scaling, low-latency require- ments for requests, and high computation costs for different stages of the pipeline. In this paper we propose that by taking a dataflow abstraction we can simplify and increase performance of serving these machine learning pipelines. The proposed system FLOWSERVEcombines this dataflow paradigm with Cloudburst, a stateful function as a service (FaaS) system to provide a framework to deploy and serve machine learning pipelines at scale. We provide several logical and physical opti- mizations that make FLOWSERVEoutperform currently used research and industry systems.}
}

EndNote citation:

%0 Thesis
%A Subbaraj, Harikaran
%T Using Dataflow for Machine Learning Inference
%I EECS Department, University of California, Berkeley
%D 2020
%8 May 28
%@ UCB/EECS-2020-75
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-75.html
%F Subbaraj:EECS-2020-75