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Writed chapter4
author Nobuyasu Oshiro <dimolto@cr.ie.u-ryukyu.ac.jp>
date Wed, 29 Jan 2014 03:37:51 +0900
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children 585196deaace
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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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32
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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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45
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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.
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51 \subsection{掲示板プログラム}
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52 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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53 に Jungle と Cassandra を利用している.
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56 \subsection{実験環境}
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57
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58 \subsubsection{ノードを実行させるサーバの仕様}
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59 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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60 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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61
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62 \begin{table}[!htbp]
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63 \caption{ノードを実行させるVMWareクラスタの仕様}
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64 \label{tab:cluster_spec_vmware}
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65 \begin{center}
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66 \begin{tabular}{|c||c|} \hline
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67 名前 & 概要 \\ \hline \hline
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68 CPU & Intel(R) Xeon(R) CPU X5650@2.67GHz \\ \hline
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69 Memory & 8GB \\ \hline
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70 OS & CentOS 5.8 \\ \hline
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71 HyperVisor & VMWare ESXi \\ \hline
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72 \end{tabular}
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73 \end{center}
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74 \end{table}
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75
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76 \begin{table}[!htbp]
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77 \caption{ノードを実行させるKVMクラスタの仕様}
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78 \label{tab:cluster_spec_kvm}
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79 \begin{center}
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80 \begin{tabular}{|c||c|} \hline
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81 名前 & 概要 \\ \hline \hline
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82 CPU & Intel(R) Xeon(R) CPU X5650@2.67GHz \\ \hline
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83 Memory & 8GB \\ \hline
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84 OS & CentOS 5.8 \\ \hline
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85 HyperVisor & KVM \\ \hline
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86 \end{tabular}
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87 \end{center}
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88 \end{table}
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89
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90 \subsubsection{1台に負荷をかけるブレードサーバの仕様}
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91 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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92 ブレードサーバの仕様を表\ref{tab:server_spec_1}に示す
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93
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94 \begin{table}[!htbp]
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95 \caption{}
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96 \label{tab:server_spec_1}
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97 \begin{center}
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98 \begin{tabular}{|c||c|} \hline
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99 名前 & 概要 \\ \hline \hline
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100 CPU & Intel(R) Xeon(R) CPU X5650@2.67GHz \\ \hline
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101 物理コア数 & 12 \\ \hline
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102 論理コア数 & 24 \\ \hline
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103 Memory & 132GB \\ \hline
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104 OS & Fedora 16 \\ \hline
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105 JavaVM & Java(TM) SE Runtime Environment (build 1.7.0-b147) \\ \hline
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106 \end{tabular}
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107 \end{center}
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108 \end{table}
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111
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112 \subsubsection{サーバの環境}
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113 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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114 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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115 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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116 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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117 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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118 \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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119 % sudo sysctl -w net.core.somaxconn=10000
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120 \end{lstlisting}
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121 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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122 \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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123 % ulimit -n 10000
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124 \end{lstlisting}
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125
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126 \section{実験結果1}
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127 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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128 weighttpに付けたオプションは以下のとおりである
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129 \begin{lstlisting}[frame=lrbt,label=src:weighttp_op,caption=weighttpのオプション,numbers=left]
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130 weighttp -n 1000000 -c 1000 -t 10 -k "http://url"
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131 \end{lstlisting}
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132 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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133
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134 実験の結果を示す.
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135 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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136 サーバノード数を表す.
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137 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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138 Cassandraのレプリケーションは5である.
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139
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140 \begin{figure}[htpb]
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141 \begin{center}
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142 \includegraphics[scale=1.0]{figures/read_bench.pdf}
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143 \caption{読み込みベンチマーク結果}
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144 \label{fig:read_cassandra}
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145 \end{center}
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146 \end{figure}
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147
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148 \begin{figure}[htpb]
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149 \begin{center}
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150 \includegraphics[scale=1.0]{figures/write_bench.pdf}
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151 \caption{書き込みベンチマーク結果}
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152 \label{fig:write_cassandra}
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153 \end{center}
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154 \end{figure}
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155
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156 読み込み, 書き込み, どちらともJungleが3倍以上の性能の差を出していることが確認できる.
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157
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158
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159
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160
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161 \section{実験結果2}
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162
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163
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164