000131364 001__ 131364 000131364 005__ 20240208115248.0 000131364 0247_ $$2doi$$a10.1145/3278720 000131364 0248_ $$2sideral$$a112376 000131364 037__ $$aART-2019-112376 000131364 041__ $$aeng 000131364 100__ $$0(orcid)0000-0001-7982-0359$$aRodríguez, Ricardo J.$$uUniversidad de Zaragoza 000131364 245__ $$aA dynamic data-throttling approach to minimize workflow imbalance 000131364 260__ $$c2019 000131364 5060_ $$aAccess copy available to the general public$$fUnrestricted 000131364 5203_ $$aScientific workflows enable scientists to undertake analysis on large datasets and perform complex scientific simulations. These workflows are often mapped onto distributed and parallel computational infrastructures to speed up their executions. Prior to its execution, a workflow structure may suffer transformations to accommodate the computing infrastructures, normally involving task clustering and partitioning. However, these transformations may cause workflow imbalance because of the difference between execution task times (runtime imbalance) or because of unconsidered data dependencies that lead to data locality issues (data imbalance). In this article, to mitigate these imbalances, we enhance the workflow lifecycle process in use by introducing a workflow imbalance phase that quantifies workflow imbalance after the transformations. Our technique is based on structural analysis of Petri nets, obtained by model transformation of a data-intensive workflow, and Linear Programming techniques. Our analysis can be used to assist workflow practitioners in finding more efficient ways of transforming and scheduling their workflows. Moreover, based on our analysis, we also propose a technique to mitigate workflow imbalance by data throttling. Our approach is based on autonomic computing principles that determine how data transmission must be throttled throughout workflow jobs. Our autonomic data-throttling approach mainly monitors the execution of the workflow and recompute data-throttling values when certain watchpoints are reached and time derivation is observed. We validate our approach by a formal proof and by simulations along with the Montage workflow. Our findings show that a dynamic data-throttling approach is feasible, does not introduce a significant overhead, and minimizes the usage of input buffers and network bandwidth. 000131364 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T35-17D$$9info:eu-repo/grantAgreement/ES/DGA/T21-17R-DISCO$$9info:eu-repo/grantAgreement/EC/H2020/644869/EU/Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements/DICE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 644869-DICE$$9info:eu-repo/grantAgreement/ES/MICINN/TIN2014-58457-R 000131364 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000131364 590__ $$a1.598$$b2019 000131364 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b54 / 107 = 0.505$$c2019$$dQ3$$eT2 000131364 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b112 / 155 = 0.723$$c2019$$dQ3$$eT3 000131364 592__ $$a0.675$$b2019 000131364 593__ $$aComputer Networks and Communications$$c2019$$dQ1 000131364 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000131364 700__ $$0(orcid)0000-0003-3057-6273$$aTolosana-Calasanz, Rafael$$uUniversidad de Zaragoza 000131364 700__ $$aRana, Omer F. 000131364 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000131364 773__ $$g19, 3 (2019), 32 [21 pp.]$$pACM Transactions on Internet Technology$$tACM Transactions on Internet Technology$$x1533-5399 000131364 8564_ $$s1230194$$uhttps://zaguan.unizar.es/record/131364/files/texto_completo.pdf$$yPostprint 000131364 8564_ $$s1919433$$uhttps://zaguan.unizar.es/record/131364/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000131364 909CO $$ooai:zaguan.unizar.es:131364$$particulos$$pdriver 000131364 951__ $$a2024-02-08-10:06:05 000131364 980__ $$aARTICLE