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Communication Dans Un Congrès Année : 2018

An efficient multi-objective genetic algorithm for cloud computing: NSGA-G

Trung-Dung Le
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Verena Kantere
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Résumé

Cloud computing provides computing resources with elasticity following a pay-as-you-go model. This raises Multi-Objective Optimization Problems (MOOP), in particular to find Query Execution Plans (QEPs) with respect to users' preferences being for example response time, money, quality, etc. In such a context, MOOP may generate Pareto-optimal front with high complexity. Pareto-dominated based Multi-objective Evolutionary Algorithms (MOEA) are often used as an alternative solution, like Non-dominated Sorting Genetic Algorithms (NSGAs) that provide better computational complexity. This paper presents NSGA-G, a NSGA based on Grid Partitioning for improving complexity and quality of current NSGAs. Experiments on DTLZ test problems using Generational Distance (GD), Inverted Generational Distance (IGD) and Maximum Pareto Front Error prove the relevance of our solution.
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Dates et versions

hal-01962235 , version 1 (20-12-2018)

Identifiants

  • HAL Id : hal-01962235 , version 1

Citer

Trung-Dung Le, Verena Kantere, Laurent d'Orazio. An efficient multi-objective genetic algorithm for cloud computing: NSGA-G. International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD), Dec 2018, Seattle, United States. ⟨hal-01962235⟩
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