Michael Chang

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-244

December 1, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-244.pdf

Cloud computing enables operators to spin up resources on-demand, and orchestrators enables the automatic deployment of complex distributed applications. As deployment of applications in datacenter environments grows increasingly automated and accessible, the logical next step is to consider how the infrastructure can support the operator in critical configuration tasks.

In this dissertation, we build and evaluate a number of infrastructure-based approaches that automate a few of these critical configuration tasks. First, we present AutoTune, an orchestrator-based tool that tunes an application's resource allocation in order to improve end-to-end performance and resource efficiency. Next, we describe Privoxy, a system which automatically checks that SQL queries issued by web applications are compliant with operator-defined privacy policies. Finally, we build a trace-driven packet simulator that evaluates how an architectural change to the datacenter networking fabric impacts the training time of convolutional neural networks for image recognition.

Advisors: Thomas Griffiths and Sergey Levine

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BibTeX citation:

@phdthesis{Chang:EECS-2021-244,
    Author= {Chang, Michael},
    Title= {Infrastructure Support for Datacenter Applications},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-244.html},
    Number= {UCB/EECS-2021-244},
    Abstract= {Cloud computing enables operators to spin up resources on-demand, and orchestrators enables the automatic deployment of complex distributed applications. As deployment of applications in datacenter environments grows increasingly automated and accessible, the logical next step is to consider how the infrastructure can support the operator in critical configuration tasks.

In this dissertation, we build and evaluate a number of infrastructure-based approaches that automate a few of these critical configuration tasks. First, we present AutoTune, an orchestrator-based tool that tunes an application's resource allocation in order to improve end-to-end performance and resource efficiency. Next, we describe Privoxy, a system which automatically checks that SQL queries issued by web applications are compliant with operator-defined privacy policies. Finally, we build a trace-driven packet simulator that evaluates how an architectural change to the datacenter networking fabric impacts the training time of convolutional neural networks for image recognition.},
}

EndNote citation:

%0 Thesis
%A Chang, Michael 
%T Infrastructure Support for Datacenter Applications
%I EECS Department, University of California, Berkeley
%D 2021
%8 December 1
%@ UCB/EECS-2021-244
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-244.html
%F Chang:EECS-2021-244