28 lines
3.9 KiB
TeX
28 lines
3.9 KiB
TeX
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Since the introduction of the smartphone, mobile computing has become pervasive in our society, with one in every five people in the world owning a smartphone~\cite{phones}. Mobile devices, however,
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have evolved far beyond the stereotypical smartphone and tablet, and are now employed in a wide variety of domains. Of the currently available mobile systems, Android has seen the most widespread deployment outside of the consumer electronics market. Android's open source nature has prompted its ubiquitous adoption in sensing, medical, command and control, robotics, and automotive applications.
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For each of these domains, Android supports specific client-side applications that often utilize application-resident, or embedded databases such as SQLite~\cite{sqlite} to provide necessary functionality. Even Android
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itself, specifically the framework layer, makes use of embedded databases.
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The way in which mobile applications use databases, is however, rather different from traditional server farms and desktop applications that focus on ``big data.''
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Based on the experiments we present in this paper, SQLite on \textit{one} average Android smart-phone satisfies over 178 thousand database requests \textit{per day}, or about 2 requests every second.
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As such, client-side mobile applications represent a new and rapidly growing class of \textit{pocket-scale} database consumers. The execution context for these client-side application workloads varies wildly. This means that the performance of the database is not only dependent on the query workload being executed by the client-side application, but is also impacted by other applications that may have access to the database, other applications currently using system resources ({\em e.g.} processor, memory,
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file system), and the overall energy available and power profile of the system itself ({\em e.g.} voltage scaling, power governors, OS policies).
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The challenges that arise in handling pocket-scale data --- that is, minimizing power consumption, latency, and io --- are familiar ground for database practitioners, but the trade-offs and workload characteristics are far less well understood. In this paper, we present the results of a one-month trace of SQLite activity on 11 Android smartphones, part of a real-world experimental testbed called PhoneLab~\cite{phonelab}. We show that the workload characteristics encountered by SQLite on these phones differ substantially from the characteristics of database workloads expressed in popular database benchmarking suites. We argue that a new benchmark for mobile and embedded databases could spur innovation in this area, and outline the workload characteristics of such a benchmark.
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\noindent The contributions of this paper are the following:
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\begin{itemize}
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\item A detailed examination of SQLite~\cite{sqlite} usage in a real-world mobile context. We compare our gathered results on mobile application and Android framework usage of SQLite to tradition TPC benchmark
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workloads.
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\item The synthesis of an open source data set gathered over one month of "in the wild" execution of database queries using eleven test subjects and their personal smartphones. Data was gathered using PhoneLab~\cite{phonelab}.
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\item An outline of workload characteristics and other desiderata for a proposed TPC-MOBILE benchmark.
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\end{itemize}
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The remainder of the paper is organized as follows.
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We present our motivation observations in Section~\ref{sec:overview}. We outline our experimental setup and the process of obtaining data from 11 primary-use smart phones in
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Section~\ref{sec:experimental}. We discuss relevant
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workloads based on query complexity, and database activity in Section~\ref{sec:queryc} and Section~\ref{sec:dba} respectively. Based on this
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detailed analysis and a review of related work, we sketch the requirements for a TPC-MOBILE benchmark in Section~\ref{sec:pocketdata}. We wrap up with conclusions in Section~\ref{sec:conc}.
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