Research Article
Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses
Algorithm 1
Multiobjective optimization of Cloud database configuration using branch-and-bound.
Input: Set of VM types (VMs), Query workload () | Output: A set of pareto-optimal solutions | () QP: Query Plan | () Q: Queue of QPs | () Calculated_value: Multiobjective cost of optimization | () S: Set of solutions | () for ( to all configurations of (VMs)) do | () B_response_time Find_the_best_response_time (, ) | () Cheapest_cost Find_the_cheapest_cost (, ) | () Heuristic_value Calculate_heuristic_value (B_response_time, Cheapest_cost) | () Q = null; | () ; | () for (Each Query Plan QP in the workload ) do | () Q.Enqueue QP_in(); | () Calculated_value = Calculate_with (, Q); | () if (Calculated_value is worse than Heuristic_value}) then | () Break the loop and start with the next VM configuration; | () if (is empty and the Calculated_value is better than heuristic_value) then | () Add () to the solution set of ; | () return ; |
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