Por favor, use este identificador para citar o enlazar este ítem: https://repositorio.cetys.mx/handle/60000/91
Título : Adaptive Resource Allocation with Job Runtime Uncertainty
Otros títulos : Journal of Grid Computing
Autor : Ramirez Velarde, Raúl
Tchernykh, Andrei
Barba Jimenez, Carlos
Hirales Carbajal, Adan
Palabras clave : Runtime uncertainty;Distributed system;Resource allocation;Self-similarity;Heavy tails
Fecha de publicación : oct-2017
Citación : 15;4
Resumen : In this paper, we address the problem of dynamic resource allocation in presence of job run- time uncertainty. We develop an execution delay model for runtime prediction, and design an adaptive stochastic allocation strategy, named Pareto Fractal Flow Predictor (PFFP). We conduct a comprehensive performance evaluation study of the PFFP strategy on real production traces, and compare it with other well-known non-clairvoyant strategies over two metrics. In order to choose the best strategy, we perform bi-objective analysis according to a degradation methodology. To analyze possible biasing results and negative effects of allowing a small portion of theproblem instances with large deviation to dominate the conclusions, we present performance profiles of the strategies. We show that PFFP performs well in different scenarios with a variety of workloads and distributed resources.
metadata.dc.description.url: https://link.springer.com/article/10.1007/s10723-017-9410-6
URI : https://repositorio.cetys.mx/handle/60000/91
ISSN : 1572-9184
Aparece en las colecciones: Artículos de Revistas

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
Ramírez-Velarde2017_Article_AdaptiveResourceAllocationWith.pdfAdaptive Resource Allocation with Job Runtime Uncertainty1.56 MBAdobe PDFVista previa
Visualizar/Abrir


Este ítem está protegido por copyright original



Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons