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.

Advisors: Joseph Gonzalez

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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