Performance evaluation of a parallel tabu search task scheduling algorithm

Porto, Stella C. S.; Kitajima, João Paulo; Ribeiro, Celso C.

doi:10.1016/s0167-8191(99)00096-4

Cited by 22 publications

(13 citation statements)

References 16 publications

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“…Los trabajos en (Porto and Ribeiro, 1995;Porto et al, 2000) proponen tabu search (TS) (Glover, 1986) para desplegar tareas en computadores y crear planificaciones no expulsoras en sistemas de tiempo real distribuidos con el objetivo de minimizar el tiempo total de la planificación pero no el número de computadores usados. En (Chen and Lin, 2000) se usa TS para desplegar tareas en computadores con el objetivo de minimizar el tráfico de red y el número de computadores usados pero sin llevar a cabo una planificación explícita.…”

Section: Trabajos Relacionadosunclassified

Algoritmo genético permutacional para el despliegue y la planificación de sistemas de tiempo real distribuidos

Azketa

Gutiérrez

Natale

et al. 2013

Revista Iberoamericana de Automática e Informática Industrial R

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El despliegue y la planificación de tareas y mensajes en sistemas de tiempo real distribuidos son problemas NP-difíciles (NPhard), por lo que no existen métodosóptimos para solucionarlos en tiempo polinómico. En consecuencia, estos problemas son adecuados para abordarse mediante algoritmos genéricos de búsqueda y optimización. En este artículo se propone un algoritmo genético multiobjetivo basado en una codificación permutacional de las soluciones para abordar el despliegue y la planificación de sistemas de tiempo real distribuidos. Además de desplegar tareas en computadores y de planificar tareas y mensajes, este algoritmo puede minimizar el número de computadores utilizados, la cantidad de recursos computacionales y de comunicaciones empleados y el tiempo de respuesta de peor caso medio de las aplicaciones. Los resultados experimentales muestran que este algoritmo genético permutacional puede desplegar y planificar sistemas de tiempo real distribuidos de forma satisfactoria y en tiempos razonables.

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Section: Trabajos Relacionadosunclassified

Algoritmo genético permutacional para el despliegue y la planificación de sistemas de tiempo real distribuidos

Azketa

Gutiérrez

Natale

et al. 2013

Revista Iberoamericana de Automática e Informática Industrial R

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“…The short term memory consists of a tabu list that stores information regarding the most recently accessed solutions, so that, any solutions matching a case in the tabu list are summarily rejected. The following publications reviewed some task planning cases in which the application of these algorithms had an effective result (Chambers & Barnes, 1996;Porto, Kitajima, & Ribeiro, 1996;Taillard, 1994).…”

Section: Tabu Searchmentioning

confidence: 99%

“…As a result, it is necessary to either resort to heuristics to solve optimization problems (Wu et al, 2002) in which the parameters of the objective function are calculated, or to combine heuristic techniques with linear programming to solve these types of problems (Verter & Dincer, 1992). It is advisable to use metaheuristic techniques (Kim & Park, 2004;Kolonko, 2009;Porto, Kitajima, & Ribeiro, 1996;Taillard, 1994) to solve these types of problems, as they can obtain efficient solutions in reasonable execution times.…”

Section: Introductionmentioning

confidence: 99%

Model for assigning roles automatically in egovernment virtual organizations

Zato¹,

Paz²,

Reboredo³

et al. 2012

Expert Systems with Applications

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a b s t r a c tWhen working in conjunction with multiagent systems, virtual organizations attempt to simulate the functions and interactions of entities in different environments. Recent studies have addressed the problem of assigning roles to agents that form part of the organization, and incorporating new agents to carry out certain tasks. However, these studies are limited to defining the norms and rules that determine the behavior of the organization. The present study proposes a virtual organization model for egovernment environments to assign resources and minimize the required personnel by forecasting workloads. To this end, a neural network, queuing theory, and CBR are used to obtain an efficient distribution. Queuing theory can establish the number of agents with a specific role that are necessary to maximize profits, while the network distributes roles among agents according to their respective efficiency. The final part of the paper is focused on validating the plan developed inside a case study centered on e-government in order to obtain empirical results.

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“…The problem becomes more complicated due to non deterministic nature of task application model and heterogeneous resource environments. A plethora of heuristics such as clustering algorithms (Palis et al 1996, Topcuoglu et al 2002, list scheduling algorithms (Augonnet et al 2011, Topcuoglu et al 1999, task duplication based algorithms (Hagras & Janeèek 2005, Park & Choe, 2001, Ranaweera & Agrawal, 2000, genetic algorithms (Oh&Wu, 2004, Ulusoy 2004, simulated annealing (Braun et al 2001, Kazem et al 2008, Wanneng & Shijue 2006, tabu search (Porto et al 2000, Porto & Ribeiro 1995 and particle swam optimization (Jarboui et al 2008, Salman et al 2002 have been proposed in literature for the optimal solution of scheduling problem. Static (Shirazi et al 1990) as well as dynamic scheduling (Page & Naughton 2005, Rotithor 1994 schemes are generally employed for the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

A Survey on ANN Based Task Scheduling Strategies in Heterogeneous Distributed Computing Systems

Hussain

Azam

Sharif

et al. 2016

Nepal Journal of Science and Technology

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Heterogeneous Distributed Computing Systems (HeDCS) efficiently utilize the heterogeneity of diverse computational resources which are interlinked through high speed networks for executing a group of computing intensive applications. Directed acyclic graphs (DAGs) are usually used to represent these parallel applications with varied computational requirements and constraints. The optimal scheduling of the given set of precedence constrained tasks to available resources is a core concern in HeDCS and is known to be NP Complete problem. Non deterministic nature of application programs and heterogeneous environment are the main challenges in designing, implementing and analyzing phases of task scheduling techniques. A myriad of heuristic and meta-heuristic approaches have been proposed in the literature to solve this complex problem. The basic purpose of this study is to cover ANN based task scheduling strategies in the distributed computing environment perspective. Further existing scheduling heuristics could be classified in a new state of art classification including the description of frequently used parameters in the mentioned scheduling strategies. The flexible and powerful nature of ANN for identifying the data patterns, underlying time and other constraints and learning capabilities have shown to be a promising candidate among other heuristics.Nepal Journal of Science and Technology Vol. 16, No.1 (2015) pp. 69-78

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Performance evaluation of a parallel tabu search task scheduling algorithm

Cited by 22 publications

References 16 publications

Algoritmo genético permutacional para el despliegue y la planificación de sistemas de tiempo real distribuidos

Algoritmo genético permutacional para el despliegue y la planificación de sistemas de tiempo real distribuidos

Model for assigning roles automatically in egovernment virtual organizations

A Survey on ANN Based Task Scheduling Strategies in Heterogeneous Distributed Computing Systems

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