info:eu-repo/semantics/publishedVersion
Schedulers based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments
Fecha
2013Registro en:
Pacini Naumovich, Elina Rocío; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Schedulers based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments; International Gemological Institute; 2013; 410-448
9781466625181
CONICET Digital
CONICET
Autor
Pacini Naumovich, Elina Rocío
Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
Resumen
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, we 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.