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update oasys evaluation section

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Mike Demmer 2005-03-25 05:59:24 +00:00
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doc/paper2/LLADD.tex
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@ -1889,6 +1889,20 @@ most difficult to implement in another storage system.
\subsection{Recovery and Log Truncation}
\begin{figure*}
\includegraphics[%
width=1\columnwidth]{mem-pressure.pdf}
\includegraphics[%
width=1\columnwidth]{mem-pressure.pdf}
\caption{\label{fig:OASYS} \yad optimizations for object
serialization. The first graph shows the effectiveness of both the
diff-based log records and the update/flush optimization as a function
of the portion of each object that is modified. The second graph
disables the filesystem buffer cache (via O\_DIRECT) and shows the
benefits of the update/flush optimization when there is memory
pressure.}
\end{figure*}
An observant reader may have noticed a subtle problem with this
scheme. More than one object may reside on a page, and we do not
constrain the order in which the cache calls flush() to evict objects.
@ -1928,23 +1942,9 @@ operations.
\subsection{Evaluation}
\begin{figure*}
\includegraphics[%
width=1\columnwidth]{mem-pressure.pdf}
\includegraphics[%
width=1\columnwidth]{mem-pressure.pdf}
\caption{\label{fig:OASYS} \yad optimizations for object
serialization. The first graph shows the effectiveness of both the
diff-based log records and the update/flush optimization as a function
of the portion of each object that is modified. The second graph
disables the filesystem buffer cache (via O\_DIRECT) and shows the
benefits of the update/flush optimization when there is memory
pressure.}
\end{figure*}
We implemented a \yad plugin for \oasys, a C++ object serialization
library that includes various object serialization backends, including
one for Berkeley DB. We set up an experiment in which objects are
library that can use various object serialization backends.
We set up an experiment in which objects are
retrieved from a cache according to a hot-set distribution\footnote{In
an example hot-set distribution, 10\% of the objects (the hot set) are
selected 90\% of the time.} and then have certain fields modified. The
@ -1954,26 +1954,25 @@ both Berkeley DB and the various \yad configurations.
The first graph in Figure \ref{fig:OASYS} shows the time to perform
100,000 updates to the object as we vary the fraction of the object
data that is modified in each update. In the most extreme case, when
data that is modified in each update. In all
cases, we see that that the savings in log bandwidth and
buffer-pool overhead by generating diffs and having separate
update() and flush() calls outweighs the overhead of the operations.
In the most extreme case, when
only one integer field from an ~1KB object is modified, the fully
optimized \yad shows a threefold speedup over Berkeley DB.
and \ref{fig:oasys-mem}
In the second graph, we constrained the \yad buffer pool size to be a
fraction of the size of the object cache, and bypass the filesystem
buffer cache via the O\_DIRECT option. This experiment specifically
focuses on the benefits of the update() and flush() optimizations
described above. From this graph, we see that as the percentage of
requests that are serviced by the cache increases, we see that the
performance increases greatly. Furthermore, even when only 10\% of the
requests hit the cache, the optimized update() / flush() \yad variant
achieves almost equivalent performance to the unoptimized \yad.
The \yad plugin makes use of the optimizations
described in this section, and was used to generate Figure~[TODO].
For comparison, we also implemented a non-optimized \yad plugin to
directly measure the effect of our optimizations.
Initially, OASYS did not support an object cache, so this
functionality was added. Berkeley DB and \yad's variants were run
using identical cache settings and random seeds for load generation.
Even though the serialization requests were serviced out of operating
system cache, we see that the optimized \yad implemenation has a
clear advantage under most circumstances, suggesting that the overhead
incurred by generating diffs and having seperate update() and flush()
calls is negligible compared to the savings in log bandwidth and
buffer-pool overhead that the optimizations provide.
\mjd{something more here?}
Ignoring the checkpointing scheme, the operations required for these
two optimizations are roughly 150 lines of C code, including
@ -1981,16 +1980,6 @@ whitespace, comments and boilerplate function registrations. Although
the reasoning required to ensure the correctness of this code was
complex, the simplicity of the implementation is encouraging.
\rcs{analyse OASYS data.}
test 1: small oasys buffer cache (23 pages), O\_DIRECT turned on
The test used 5000 objects, a cache size of 20\% of the objects, and a
hot set size of 10\% of the objects. Turns out that ratio is actually
necessary to achieve the desired effects, otherwise you will evict hot
objects more than you want. 10000 iterations.
This section uses:
\begin{enumerate}