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Added some figures
author Nobuyasu Oshiro <dimolto@cr.ie.u-ryukyu.ac.jp>
date Thu, 30 Jan 2014 16:15:32 +0900
parents 585196deaace
children c63aaa629330
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1 \chapter{分散木構造データーベース Jungle の評価}
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2 前章では Jungle における分散データベースの詳細な実装について述べた.
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3 本章では実装を行った Jungle に対して Cassandra との性能比較を行い評価をする.
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4 性能比較の為に簡易な掲示板プログラムを Jungle と Cassandra それぞれに作成した.
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5 複数のノードに繋がっている状態においても性能を測りたいため, 学科提供する
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6 VMWare の並列環境を利用する. また, 我々の研究室が利用しているブレードサーバ
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7 上で動いている KVM もノードとして利用する.
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9 \section{実験方法}
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10 実験は同じ機能を提供している簡易掲示板プログラムを Jungle と Cassandra それぞれで
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11 動かし, HTTPリクエストにより負荷をかけて行う.
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12 レスポンスが帰ってくるまでの時間をはかる.
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14 また, 実験は2つ行う.
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15 まず行う実験は, 複数のノードで起動してるうちの1つのノードに負荷をかける方法である.
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16 これはノードの数に比例してレスポンスが遅くなっていないか確かめるためである.
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17 \begin{figure}[htpb]
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18 \begin{center}
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19 \includegraphics[scale=0.70]{figures/jungle_experiment.pdf}
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20 \caption{複数起動中のJungle の1ノードへの負荷}
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21 \label{fig:jungle_exp}
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22 \end{center}
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23 \end{figure}
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25 \begin{figure}[htpb]
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26 \begin{center}
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27 \includegraphics[scale=0.70]{figures/cas_experiment.pdf}
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28 \caption{複数起動中のCassandra の1ノードへの負荷}
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29 \label{fig:cas_exp}
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30 \end{center}
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31 \end{figure}
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33 次に行う実験は複数のノードに対し複数のクライアントから負荷をかける方法である.
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34 それぞれ大量のHTTPリクエストをだし, 全てのリクエストの処理にかかる時間を測定する.
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36 クライアントの数に比例してノードを増やすことでレスポンスを維持できるか
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37 スケーラビリティを調べるためである.
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38 \begin{figure}[htpb]
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39 \begin{center}
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40 \includegraphics[scale=0.70]{figures/clients_request_servers.pdf}
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41 \caption{複数のクライアントから複数のノードへの負荷}
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42 \label{fig:clients_servers}
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43 \end{center}
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44 \end{figure}
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46 \subsection{weighttp}
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47 最初の実験で1つのノードに負荷をかけるプログラムはウェブサーバの測定ツールであるweighttpを使用する.
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48 weighttpは総リクエスト数, 同時接続数, ネイティブスレッド数をオプションとして指定することができるC言語
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49 でかかれたプログラムである.
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52 \subsection{掲示板プログラム}
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53 今回使用する掲示板プログラムは組み込み用ウェブサーバであるJettyをフロントエンドとして利用し, バックエンド
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54 に Jungle と Cassandra を利用している.
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57 \subsection{実験環境}
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58
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59 \subsubsection{ノードを実行させるサーバの仕様}
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60 使用するVMWareとKVMのクラスタの使用を以下に示す.
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61 クラスタは仕様を表\ref{tab:cluster_spec_vmware}と表\ref{tab:cluster_spec_kvm}に示す.
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62
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63 \begin{table}[!htbp]
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64 \caption{ノードを実行させるVMWareクラスタの仕様}
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65 \label{tab:cluster_spec_vmware}
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66 \begin{center}
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67 \begin{tabular}{|c||c|} \hline
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68 名前 & 概要 \\ \hline \hline
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69 CPU & Intel(R) Xeon(R) CPU X5650@2.67GHz \\ \hline
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70 Memory & 8GB \\ \hline
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71 OS & CentOS 5.8 \\ \hline
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72 HyperVisor & VMWare ESXi \\ \hline
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73 JavaVM & Java(TM) SE Runtime Environment (build 1.7.0-b147) \\ \hline
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74 \end{tabular}
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75 \end{center}
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76 \end{table}
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78 \begin{table}[!htbp]
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79 \caption{ノードを実行させるKVMクラスタの仕様}
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80 \label{tab:cluster_spec_kvm}
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81 \begin{center}
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82 \begin{tabular}{|c||c|} \hline
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83 名前 & 概要 \\ \hline \hline
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84 CPU & Intel(R) Xeon(R) CPU X5650@2.67GHz \\ \hline
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85 Memory & 8GB \\ \hline
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86 OS & CentOS 5.8 \\ \hline
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87 HyperVisor & KVM \\ \hline
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88 JavaVM & Java(TM) SE Runtime Environment (build 1.7.0-b147) \\ \hline
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89 \end{tabular}
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90 \end{center}
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91 \end{table}
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93 \subsubsection{1台に負荷をかけるブレードサーバの仕様}
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94 最初の実験で負荷をかける側としてブレードサーバを使用する.
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95 ブレードサーバの仕様を表\ref{tab:server_spec_1}に示す
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97 \begin{table}[!htbp]
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98 \caption{}
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99 \label{tab:server_spec_1}
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100 \begin{center}
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101 \begin{tabular}{|c||c|} \hline
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102 名前 & 概要 \\ \hline \hline
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103 CPU & Intel(R) Xeon(R) CPU X5650@2.67GHz \\ \hline
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104 物理コア数 & 12 \\ \hline
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105 論理コア数 & 24 \\ \hline
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106 Memory & 132GB \\ \hline
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107 OS & Fedora 16 \\ \hline
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108 \end{tabular}
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109 \end{center}
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110 \end{table}
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113
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114 \subsubsection{サーバの環境}
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115 HTTPによりノードに負荷を掛ける場合気をつけることがある.
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116 それはサーバの設定により最大コネクション数や開くことのできるファイル記述子の数に制限がかかっていることである.
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117 この2つの値はデフォルトでは小さなものとなっており, そのままではカーネル
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118 の設定がネックとなったベンチマーク結果がでる可能性がある.
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119 そこで次のようにコマンドを実行することでコネクション数の制限を増やすことができる.
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120 \begin{lstlisting}[frame=lrbt,label=src:maxconn_up,caption=コネクション数を増やす,numbers=left]
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121 % sudo sysctl -w net.core.somaxconn=10000
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122 \end{lstlisting}
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123 ファイル記述子の制限を増やす場合は次のコマンドを実行する
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124 \begin{lstlisting}[frame=lrbt,label=src:max_up_filedisc,caption=ファイル記述子の制限を増やす,numbers=left]
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125 % ulimit -n 10000
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126 \end{lstlisting}
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127
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128 \section{実験結果1}
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129 サーバノード数は10台から50台まで10台単位でweighttpにより負荷をかけ測定した.
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130 weighttpに付けたオプションは以下のとおりである
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131 \begin{lstlisting}[frame=lrbt,label=src:weighttp_op,caption=weighttpのオプション,numbers=left]
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132 weighttp -n 1000000 -c 1000 -t 10 -k "http://url"
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133 \end{lstlisting}
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134 ネイティブスレッドを10個生成し, 同時接続は1000までで, 百万リクエストを送るオプションとなっている.
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135
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136 実験の結果を示す.
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137 縦軸は全てのリクエストに対してレスポンスが返ってくるのにかかった時間(秒), 横軸は
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138 サーバノード数を表す.
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139 Jungle と, Cassandra のコンシステンシー・レベルをQUORUM, ONEと両方の結果を測定した.
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140 Cassandraのレプリケーションは5である.
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141
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142 \begin{figure}[htpb]
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143 \begin{center}
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144 \includegraphics[scale=1.0]{figures/read_bench.pdf}
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145 \caption{読み込みベンチマーク結果}
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146 \label{fig:read_cassandra}
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147 \end{center}
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148 \end{figure}
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149
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150 \begin{figure}[htpb]
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151 \begin{center}
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152 \includegraphics[scale=1.0]{figures/write_bench.pdf}
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153 \caption{書き込みベンチマーク結果}
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154 \label{fig:write_cassandra}
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155 \end{center}
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156 \end{figure}
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157
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158 読み込み, 書き込み, どちらともJungleが3倍以上早くレスポンスを返していることが確認できる.
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159 また, CassandraもJungleもノードの数が増えてもレスポンスを返す時間が遅くならないことも分かる.
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160
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161
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162
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163
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164 \section{実験結果2}
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165 学科の並列環境クラスタを用いて分散環境下での実験を行う
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166 学科の提供するVMは48台だが, ブレードサーバ上で動くKVMから12台を利用し, 合計60台を使用する.
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167 JungleとCassandraをそれぞれサーバノード10台, 20台, 30台で動かし, クライアントも10台, 20台, 30台
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168 と増やして負荷をかける.
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169 KVM側はクライアント側だけに利用する.
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170 weighttpに付けたオプションを以下の通りである.
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171 \begin{lstlisting}[frame=lrbt,label=src:distributed_weighttp_op,caption=weighttpのオプション（実験2）,numbers=left]
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172 weighttp -n 50000 -c 200 -t 2 -k "http://url"
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173 \end{lstlisting}
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174 クライアント1台からはそれぞれ5万のHTTPリクエストが送られる.
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175 実験1に比べ同時接続数とネイティブスレッド数が少ないのはVMの環境に合わせてあるからである.
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176
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177 測定は読み込みと書き込みの両方を行う.
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178 測定の結果をグラフにしたのを図\ref{fig:distributed_read_bench}, \ref{fig:distributed_write_bench}に示す.
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179
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180 \begin{figure}[htpb]
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181 \begin{center}
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182 \includegraphics[scale=1.0]{figures/distributed_read_bench.pdf}
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183 \caption{分散環境下における読み込みベンチマーク結果}
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184 \label{fig:distributed_read_bench}
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185 \end{center}
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186 \end{figure}
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187
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188 \begin{figure}[htpb]
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189 \begin{center}
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190 \includegraphics[scale=1.0]{figures/distributed_write_bench.pdf}
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191 \caption{分散環境下における書き込みベンチマーク結果}
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192 \label{fig:distributed_write_bench}
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193 \end{center}
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194 \end{figure}
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195
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196
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197
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198
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199