dc.contributorBhattacharyya, Santanu
dc.contributorDutta, P.
dc.creatorPacini Naumovich, Elina Rocío
dc.creatorMateos Diaz, Cristian Maximiliano
dc.creatorGarcia Garino, Carlos Gabriel
dc.date.accessioned2021-06-01T03:30:54Z
dc.date.accessioned2022-10-15T15:05:41Z
dc.date.available2021-06-01T03:30:54Z
dc.date.available2022-10-15T15:05:41Z
dc.date.created2021-06-01T03:30:54Z
dc.date.issued2013
dc.identifierPacini 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
dc.identifier9781466625181
dc.identifierhttp://hdl.handle.net/11336/132875
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4400450
dc.description.abstractScientists 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.
dc.languageeng
dc.publisherInternational Gemological Institute
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.4018/978-1-4666-2518-1.ch016
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.igi-global.com/gateway/chapter/72502
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceHandbook of Research on Computational Intelligence for Engineering, Science and Business
dc.subjectPARAMETER SWEEP
dc.subjectJOB SCHEDULING
dc.subjectGRID COMPUTING
dc.subjectCLOUD COMPUTING
dc.subjectSWARM INTELLIGENCE
dc.subjectANT COLONY OPTIMIZATION
dc.subjectMAKESPAN
dc.subjectLOAD BALANCING
dc.titleSchedulers based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeinfo:eu-repo/semantics/bookPart
dc.typeinfo:ar-repo/semantics/parte de libro


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