awstream.tex

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\input{meta}

\begin{document}

\copyrightyear{2018}
\acmYear{2018}
\setcopyright{acmcopyright}
\acmConference[SIGCOMM '18]{ACM SIGCOMM 2018 Conference}{August 20--25, 2018}{Budapest, Hungary}
\acmBooktitle{SIGCOMM '18: ACM SIGCOMM 2018 Conference, August 20--25, 2018, Budapest, Hungary}
\acmPrice{15.00}
\acmDOI{10.1145/3230543.3230554}
\acmISBN{978-1-4503-5567-4/18/08}

\title{\sysname{}: Adaptive Wide-Area Streaming Analytics}

\author{Ben Zhang}
\affiliation{UC Berkeley}

\author{Xin Jin}
\affiliation{Johns Hopkins University}

\author{Sylvia Ratnasamy}
\affiliation{UC Berkeley}

\author{John Wawrzynek}
\affiliation{UC Berkeley}

\author{Edward A. Lee}
\affiliation{UC Berkeley}

\renewcommand{\shortauthors}{B. Zhang et al.}

\begin{abstract}

The emerging class of wide-area streaming analytics faces the challenge of
scarce and variable WAN bandwidth. Non-adaptive applications built with TCP or
UDP suffer from increased latency or degraded accuracy. State-of-the-art
approaches that adapt to network changes require developer writing sub-optimal
manual policies or are limited to application-specific optimizations.

We present \sysname{}, a stream processing system that simultaneously achieves
low latency and high accuracy in the wide area, requiring minimal developer
efforts. To realize this, \sysname{} uses three ideas: $(i)$ it integrates
application adaptation as a first-class programming abstraction in the stream
processing model; $(ii)$ with a combination of offline and online profiling, it
automatically learns an accurate profile that models accuracy and bandwidth
trade-off; and $(iii)$ at runtime, it carefully adjusts the application data
rate to match the available bandwidth while maximizing the achievable
accuracy. We evaluate \sysname{} with three real-world applications: augmented
reality, pedestrian detection, and monitoring log analysis. Our experiments show
that \sysname{} achieves sub-second latency with only nominal accuracy drop
(2-6\%).

\end{abstract}

\begin{CCSXML}
<ccs2012>
  <concept>
    <concept_id>10003033.10003039.10003051</concept_id>
    <concept_desc>Networks~Application layer protocols</concept_desc>
    <concept_significance>500</concept_significance>
  </concept>
  <concept>
    <concept_id>10003033.10003039.10003056</concept_id>
    <concept_desc>Networks~Cross-layer protocols</concept_desc>
    <concept_significance>500</concept_significance>
  </concept>
  <concept>
    <concept_id>10003033.10003079.10003080</concept_id>
    <concept_desc>Networks~Network performance modeling</concept_desc>
    <concept_significance>500</concept_significance>
  </concept>
  <concept>
    <concept_id>10003033.10003083.10003084.10003088</concept_id>
    <concept_desc>Networks~Wide area networks</concept_desc>
    <concept_significance>500</concept_significance>
  </concept>
  <concept>
    <concept_id>10003033.10003083.10003094</concept_id>
    <concept_desc>Networks~Network dynamics</concept_desc>
    <concept_significance>500</concept_significance>
  </concept>
  <concept>
    <concept_id>10003033.10003106.10010924</concept_id>
    <concept_desc>Networks~Public Internet</concept_desc>
    <concept_significance>500</concept_significance>
  </concept>
</ccs2012>
\end{CCSXML}

\ccsdesc[500]{Networks~Application layer protocols}
\ccsdesc[500]{Networks~Cross-layer protocols}
\ccsdesc[500]{Networks~Network performance modeling}
\ccsdesc[500]{Networks~Wide area networks}
\ccsdesc[500]{Networks~Network dynamics}
\ccsdesc[500]{Networks~Public Internet}

\keywords{Wide Area Network; Adaptation; Learning; Profiling}

\maketitle

\input{sections/intro}
\input{sections/motivation}
\input{sections/system}
\input{sections/impl}
\input{sections/evaluation}
\input{sections/discussion}
\input{sections/related}
\input{sections/conclusion}
\input{sections/acknowledgment}
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\bibliography{awstream}

\end{document}

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