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Schedulers Based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments
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Author(s): Elina Pacini (Institute for Information and Communication Technologies, Universidad Nacional de Cuyo, Argentina), Cristian Mateos (Instituto Superior de Ingenieria de Software, Consejo Nacional de Investigaciones Cientificas y Tecnicas, Argentina)and Carlos García Garino (Institute for Information and Communication Technologies, Universidad Nacional de Cuyo, Argentina)
Copyright: 2013
Pages: 39
Source title:
Handbook of Research on Computational Intelligence for Engineering, Science, and Business
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-4666-2518-1.ch016
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Abstract
Scientists and engineers are more and more faced to the need of computational power to satisfy the ever-increasing resource intensive nature of their experiments. An example of these experiments is Parameter Sweep Experiments (PSE). PSEs involve many independent jobs, since the experiments are executed under multiple initial configurations (input parameter values) several times. In recent years, technologies such as Grid Computing and Cloud Computing have been used for running such experiments. However, for PSEs to be executed efficiently, it is necessary to develop effective scheduling strategies to allocate jobs to machines and reduce the associated processing times. Broadly, the job scheduling problem is known to be NP-complete, and thus many variants based on approximation techniques have been developed. In this work, the authors conducted a survey of different scheduling algorithms based on Swarm Intelligence (SI), and more precisely Ant Colony Optimization (ACO), which is the most popular SI technique, to solve the problem of job scheduling with PSEs on different distributed computing environments.
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