Algoritmo aleatorizado basado en distribuciones deslizantes para el problema de planificación en sistemas Grid
Randomized Algorithm based on Sliding Distributions for the Scheduling Problem in Grid Systems
Héctor J. Selley-Rojas1,2, Jesús García-Díaz1, Manuel A. Soto-Ramos1, Felipe R. Menchaca-García3 and Rolando Menchaca-Mendez1
1 Instituto Politécnico Nacional, Centro de Investigación en Computación, México DF, México. hector.selley@gmail.com, jesgadiaz@gmail.com, yakovichs@gmail.com, rmen@cic.ipn.mx
]]> 2 Universidad Anáhuac Norte, Facultad de Ingeniería, México DF, México.3 Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, México DF, México. fmenchac@gmail.com
Autor de correspondencia es Héctor J. Selley-Rojas.
Article received on 28/04/2014.
Accepted on 09/10/2014.
Resumen
En este artículo se presenta un algoritmo aleatorizado para el problema de planificación de tareas compuestas por procesos con restricciones de precedencia en ambientes distribuidos tipo Grid. El algoritmo aleatorizado propuesto esta basado en una nueva técnica que hemos denominado como de distribuciones deslizantes, la cual busca combinar las ventajas de los algoritmos de aproximación deterministas y de los algoritmos aleatorizados tipo Montecarlo. El objetivo es proveer un algoritmo que con alta probabilidad entregue soluciones p-aproximadas, pero que al mismo tiempo tenga la capacidad de analizar el vecindario extendido de dichas soluciones para escapar de máximos o mínimos locales. En el artículo se demuestra que el algoritmo propuesto es correcto y se caracteriza de manera formal su complejidad temporal. Así mismo, se evalúa el desempeño del algoritmo por medio de una serie de experimentos basados en simulaciones. Los experimentos muestran que el algoritmo propuesto logra en general un desempeño superior al de los algoritmos que componen el estado del arte en planificación en sistemas Grid. Las métricas de desempeño utilizadas son retardo promedio, retardo máximo y utilización de la Grid.
Palabras clave: Optimización combinatoria, algoritmo aleatorio, sistemas Grid, planificación de tareas.
]]>Abstract
In this paper we present a randomized algorithm for the online version of the Job Shop problem where jobs are composed of processes with precedence constraints and processors are organized in a Grid topology. The proposed randomized algorithm is based on a new technique that we have denominated as sliding distributions, which aims at combining the advantages of the deterministic approximation algorithms with those of the Montecarlo randomized algorithms. The objective is to provide an algorithm that delivers p-approximated solutions with high probability, but at the same time, is able to investigate an extended neighborhood of such solutions so that it can escape from local extrema. We formally characterize the temporal complexity of the proposed algorithm and show that it is correct. We also evaluate the performance of the proposed algorithm by means of a series of simulation-based experiments. The results show that the proposed algorithm outperforms the traditional state of the art algorithms for scheduling in Grid systems. The performance metrics are average delay, maximum delay, and Grid utilization.
Keywords: Combinatorial optimization, randomized algorithm, Grid systems, scheduling.
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