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A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

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A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

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dc.contributor.author Cai, Zhicheng es_ES
dc.contributor.author Li, Xiaoping es_ES
dc.contributor.author Ruiz García, Rubén es_ES
dc.contributor.author Li, Qianmu es_ES
dc.date.accessioned 2020-10-05T06:59:52Z
dc.date.available 2020-10-05T06:59:52Z
dc.date.issued 2017-06 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/151092
dc.description.abstract [EN] Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms. (C) 2017 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and 61572127), the Natural Science Foundation ofJiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, Grant No. 30916014107), the Fundamental Research Funds for the Central University (Grant No. 30916015104). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (No. DP12015-65895-R) co-financed by FEDER funds. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Cloud computing es_ES
dc.subject Scheduling es_ES
dc.subject Workflow es_ES
dc.subject Bag of tasks es_ES
dc.subject Stochastic es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2017.01.020 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//61572127/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Natural Science Foundation of Jiangsu Province//BK20160846/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Jiangsu Key Laboratory of Image and Video Understanding for Social Safety//30916014107/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2015-65895-R/ES/OPTIMIZATION OF SCHEDULING PROBLEMS IN CONTAINER YARDS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//61602243/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//30916015104/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Cai, Z.; Li, X.; Ruiz García, R.; Li, Q. (2017). A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Future Generation Computer Systems. 71:57-72. https://doi.org/10.1016/j.future.2017.01.020 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.future.2017.01.020 es_ES
dc.description.upvformatpinicio 57 es_ES
dc.description.upvformatpfin 72 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 71 es_ES
dc.relation.pasarela S\353567 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder National Natural Science Foundation of China es_ES
dc.contributor.funder Natural Science Foundation of Jiangsu Province es_ES
dc.contributor.funder Fundamental Research Funds for the Central Universities es_ES
dc.contributor.funder Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, China es_ES


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