title: Evolutionary multiobjective query workload optimization of Cloud data warehouses.
abstract: With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose.
authors.affiliation: METU Computer Engineering Department, Cankaya, 06800 Ankara, Turkey.
authors.forename: Tansel
authors.initials: T
authors.lastname: Dokeroglu
authors.affiliation: METU Computer Engineering Department, Cankaya, 06800 Ankara, Turkey.
authors.forename: Seyyit Alper
authors.initials: SA
authors.lastname: Sert
authors.affiliation: Hacettepe University Computer Engineering Department, Beytepe, 06800 Ankara, Turkey.
authors.forename: Muhammet Serkan
authors.initials: MS
authors.lastname: Cinar
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval; D020407:Internet