Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td align="left">Dimension of workflows</td><td align="center">Number of workflows/number of processors</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td align="left">Small</td><td align="center">500/21</td></tr><tr><td align="left">Medium</td><td align="center">1000/21</td></tr><tr><td align="left">Large</td><td align="center">2000/21</td></tr><tr><td align="left">Very large</td><td align="center">5000/21</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

The Scientific World Journal

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Table 2

Table 2: Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids 

Table 2 | Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids 