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xiii | |
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xvii | |
Foreword |
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xix | |
Preface |
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xxi | |
Warranty |
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xxiii | |
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1 | (28) |
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1 | (1) |
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2 | (1) |
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3 | (1) |
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4 | (1) |
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5 | (1) |
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6 | (16) |
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Grid middleware (core services) |
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6 | (5) |
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Grid resource brokers and schedulers |
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11 | (3) |
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14 | (2) |
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Grid programming environments |
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16 | (2) |
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18 | (4) |
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22 | (1) |
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23 | (1) |
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24 | (5) |
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Synchronization protocols for sharing resources in grid environments |
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29 | (38) |
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29 | (2) |
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Token-based mutual exclusion algorithms |
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31 | (5) |
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31 | (2) |
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33 | (1) |
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Suzuki-Kasami's algorithm |
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34 | (2) |
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Mutual exclusion algorithms for large configurations |
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36 | (3) |
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36 | (1) |
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Composition-based approach |
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37 | (2) |
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Composition approach to mutual exclusion algorithms |
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39 | (4) |
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41 | (2) |
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Composition properties and its natural effects |
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43 | (4) |
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Filtering and aggregation |
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43 | (2) |
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Preemption and structural effects |
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45 | (1) |
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Natural effects of composition |
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46 | (1) |
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47 | (15) |
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47 | (2) |
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Performance results: composition study |
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49 | (7) |
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The impact of the grid architecture |
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56 | (6) |
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62 | (1) |
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63 | (4) |
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Data replication in grid environments |
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67 | (34) |
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67 | (1) |
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68 | (8) |
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69 | (1) |
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Replication in peer-to-peer systems |
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70 | (1) |
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Replication in web environments |
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71 | (1) |
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Replication in data grids |
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72 | (4) |
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76 | (2) |
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Selective-rank model for a replication system |
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78 | (4) |
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79 | (1) |
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Estimating the availability of files |
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80 | (1) |
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80 | (2) |
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Selective-rank replication algorithm |
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82 | (3) |
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82 | (1) |
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82 | (1) |
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MaxDAR optimizer algorithm |
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83 | (2) |
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85 | (9) |
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87 | (1) |
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87 | (7) |
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94 | (1) |
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95 | (6) |
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101 | (24) |
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101 | (2) |
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From data sources to databases...to data sources |
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103 | (1) |
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Positioning the data management in grids within distributed systems |
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104 | (2) |
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Links with the other services of the middleware |
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106 | (1) |
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Problems and some solutions |
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107 | (9) |
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Data identification, indexing, metadata |
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107 | (2) |
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Data access, interoperability, query processing, transactions |
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109 | (2) |
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111 | (1) |
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Placement, replication, caching |
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112 | (1) |
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Security: transport, authentication, access control, encryption |
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113 | (2) |
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115 | (1) |
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Toward pervasive, autonomic and on-demand data management |
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116 | (1) |
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117 | (1) |
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118 | (7) |
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Future of grids resources management |
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125 | (18) |
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125 | (1) |
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Several computing paradigms |
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126 | (3) |
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126 | (1) |
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127 | (1) |
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127 | (1) |
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128 | (1) |
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Definition of cloud computing |
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129 | (1) |
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129 | (1) |
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130 | (1) |
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130 | (4) |
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130 | (2) |
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132 | (2) |
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Cloud resource management |
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134 | (3) |
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Comparison with grid systems |
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134 | (1) |
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135 | (1) |
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136 | (1) |
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Future direction of resource scheduling |
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137 | (2) |
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138 | (1) |
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138 | (1) |
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138 | (1) |
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139 | (1) |
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140 | (3) |
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Fault-tolerance and availability awareness in computational grids |
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143 | (34) |
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143 | (3) |
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Background and definitions |
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146 | (3) |
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Grid architecture and execution model |
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147 | (1) |
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148 | (1) |
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148 | (1) |
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Multi-objective scheduling for safety |
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149 | (4) |
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149 | (1) |
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150 | (2) |
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152 | (1) |
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Stable memory-based protocols |
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153 | (3) |
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Log-based rollback recovery |
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153 | (2) |
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Checkpoint-based rollback recovery |
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155 | (1) |
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Stochastic checkpoint model analysis issues |
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156 | (7) |
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Completion time without fault tolerance |
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157 | (2) |
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Impact of checkpointing on the completion time |
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159 | (4) |
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163 | (5) |
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164 | (1) |
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Fault-tolerance protocol implementations |
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164 | (2) |
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Implementation comparison |
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166 | (2) |
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168 | (2) |
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170 | (7) |
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Fault tolerance for distributed scheduling in grids |
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177 | (30) |
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177 | (2) |
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Fault tolerance in distributed systems |
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179 | (1) |
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Distributed scheduling model |
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180 | (3) |
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180 | (1) |
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181 | (1) |
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182 | (1) |
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Fault detection and repairing in the tree structure |
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183 | (6) |
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183 | (1) |
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183 | (5) |
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Messages treatment analysis |
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188 | (1) |
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Distributed scheduling algorithm |
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189 | (2) |
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Distributed dynamic scheduling algorithm with fault tolerance (DDFT) |
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189 | (1) |
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Algorithm fault tolerance issues |
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190 | (1) |
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SimGrid and simulation design |
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191 | (1) |
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192 | (7) |
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193 | (1) |
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Comparison with centralized scheduling |
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193 | (4) |
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Fault tolerance experiments |
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197 | (1) |
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197 | (2) |
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199 | (1) |
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200 | (1) |
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201 | (6) |
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207 | (28) |
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207 | (1) |
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208 | (3) |
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Heuristics for broadcasting |
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211 | (9) |
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Basic approaches for broadcasting in homogeneous environments |
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212 | (1) |
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Advanced approaches for heterogeneous clusters |
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213 | (1) |
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214 | (1) |
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New approach for broadcasting in clusters and hyper clusters |
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215 | (5) |
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Related work and related methods |
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220 | (10) |
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Broadcasting and dynamic programming |
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220 | (3) |
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223 | (5) |
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228 | (2) |
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Broadcast and heterogeneous systems |
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230 | (1) |
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230 | (2) |
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232 | (3) |
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Load balancing algorithms for dynamic networks |
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235 | (38) |
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235 | (2) |
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A taxonomy for load balancing |
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237 | (3) |
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Distributed load balancing algorithms for static networks |
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240 | (10) |
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Network model and performance measures |
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240 | (2) |
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242 | (4) |
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246 | (2) |
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248 | (2) |
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250 | (1) |
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Distributed load balancing algorithms for dynamic networks |
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250 | (7) |
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Adaption to dynamic networks |
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251 | (1) |
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Generalized adaptive exchange (GAE) |
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251 | (4) |
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Illustrating the generalized adaptive exchange most to least loaded policy on a dynamic network |
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255 | (2) |
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257 | (4) |
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On synchronous and asynchronous approaches |
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257 | (2) |
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How to define the load for some applications |
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259 | (1) |
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Implementation of static algorithms |
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259 | (1) |
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Implementation of dynamic algorithms |
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260 | (1) |
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A practical example: the advection diffusion application |
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261 | (7) |
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Load balancing and the application |
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264 | (2) |
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Load balancing in a dynamic network |
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266 | (2) |
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268 | (1) |
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269 | (4) |
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A Implementation of the replication strategies in OptorSim |
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273 | (6) |
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273 | (1) |
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274 | (1) |
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274 | (2) |
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274 | (1) |
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275 | (1) |
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How to execute the simulation |
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276 | (3) |
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B Implementation of the simulator for the distributed scheduling model |
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279 | (4) |
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279 | (1) |
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279 | (1) |
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280 | (2) |
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280 | (1) |
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280 | (2) |
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How to execute the simulation |
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282 | (1) |
Glossary |
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283 | (14) |
Author Index |
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297 | |