Models for video-on-demand scheduling with costs

Video-on-demand, which provides digital content as needed, supplies flexibility for the users but presents reactive challenges for the provider, as the peaks and troughs in demand lead to an inconsistent requirement of resources. The cost of keeping servers primed for demand that may not appear must...

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Autores principales:	J.-Ch. Grégoire, AngèleM. Hamel
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2016
Materias:	68M20 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/e86fdc493eff4f8d8938ca3ffe5feddc
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spelling	oai:doaj.org-article:e86fdc493eff4f8d8938ca3ffe5feddc2021-12-02T05:00:50ZModels for video-on-demand scheduling with costs2192-440610.1007/s13675-015-0059-2https://doaj.org/article/e86fdc493eff4f8d8938ca3ffe5feddc2016-05-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621000587https://doaj.org/toc/2192-4406Video-on-demand, which provides digital content as needed, supplies flexibility for the users but presents reactive challenges for the provider, as the peaks and troughs in demand lead to an inconsistent requirement of resources. The cost of keeping servers primed for demand that may not appear must be balanced against the cost of frustrating users who must wait for service. This VoD problem is a bi-objective optimization problem, minimizing cost to the provider and delay for the user. Mindful of real-world applications, we introduce a model that handles tasks of differing size (bandwidth) or value by assigning weights to these tasks, and combining the weight with the duration. In this way, we can account for differentiated tasks, in particular, premium users and variable sized tasks. We also extend our approach to account for multiple tasks on each machine.J.-Ch. GrégoireAngèleM. HamelElsevierarticle68M20Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 4, Iss 2, Pp 125-135 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	68M20 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	68M20 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 J.-Ch. Grégoire AngèleM. Hamel Models for video-on-demand scheduling with costs
description	Video-on-demand, which provides digital content as needed, supplies flexibility for the users but presents reactive challenges for the provider, as the peaks and troughs in demand lead to an inconsistent requirement of resources. The cost of keeping servers primed for demand that may not appear must be balanced against the cost of frustrating users who must wait for service. This VoD problem is a bi-objective optimization problem, minimizing cost to the provider and delay for the user. Mindful of real-world applications, we introduce a model that handles tasks of differing size (bandwidth) or value by assigning weights to these tasks, and combining the weight with the duration. In this way, we can account for differentiated tasks, in particular, premium users and variable sized tasks. We also extend our approach to account for multiple tasks on each machine.
format	article
author	J.-Ch. Grégoire AngèleM. Hamel
author_facet	J.-Ch. Grégoire AngèleM. Hamel
author_sort	J.-Ch. Grégoire
title	Models for video-on-demand scheduling with costs
title_short	Models for video-on-demand scheduling with costs
title_full	Models for video-on-demand scheduling with costs
title_fullStr	Models for video-on-demand scheduling with costs
title_full_unstemmed	Models for video-on-demand scheduling with costs
title_sort	models for video-on-demand scheduling with costs
publisher	Elsevier
publishDate	2016
url	https://doaj.org/article/e86fdc493eff4f8d8938ca3ffe5feddc
work_keys_str_mv	AT jchgregoire modelsforvideoondemandschedulingwithcosts AT angelemhamel modelsforvideoondemandschedulingwithcosts
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Models for video-on-demand scheduling with costs

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