A Biased Random-key Genetic Algorithm for Placement of Virtual Machines across Geo-Separated Data Centers

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Fernando Stefanello (Autor) Outra; Vaneet Aggarwal (Autor) Outra; Luciana S Buriol (Autor) Outra; Jose F Goncalves (Autor) FEP; Mauricio G C Resende (Autor) Outra

Ata de Conferência Internacional

Título: GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE

Páginas: 919-926

17th Genetic and Evolutionary Computation Conference (GECCO)

Madrid, SAN MARINO, JUL 11-15, 2015

Indexação

ISI Web of Knowledge - 10 Citações

Scopus - 12 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia industrial

Outras Informações

ID Authenticus: P-00G-FNW

DOI: 10.1145/2739480.2754768

Abstract (EN): Cloud computing has recently emerged as a new technology for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware and allowing business owners to start from small and increase resources only when there is a rise in service demand. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers, meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard Generalized Quadratic Assignment Problem (GQAP). We formalize the problem and propose a novel algorithm based on a biased random-key genetic algorithm (BRKGA) to find nearoptimal solutions for the problem. The experimental results show that the proposed algorithm is effective in quickly finding feasible solutions and it produces better results than a baseline aproach provided by a commercial solver and a multi-start algorithm.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8