131364 20240208115248.0 doi 10.1145/3278720 sideral 112376 ART-2019-112376 eng Rodríguez, Ricardo J. Universidad de Zaragoza (orcid)0000-0001-7982-0359 A dynamic data-throttling approach to minimize workflow imbalance 2019 Scientific 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. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/DGA-FSE/T35-17D info:eu-repo/grantAgreement/ES/DGA/T21-17R-DISCO info:eu-repo/grantAgreement/EC/H2020/644869/EU/Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements/DICE This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 644869-DICE info:eu-repo/grantAgreement/ES/MICINN/TIN2014-58457-R info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 1.598 2019 COMPUTER SCIENCE, SOFTWARE ENGINEERING 54 / 107 = 0.505 2019 Q3 T2 COMPUTER SCIENCE, INFORMATION SYSTEMS 112 / 155 = 0.723 2019 Q3 T3 0.675 2019 Computer Networks and Communications 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Tolosana-Calasanz, Rafael Universidad de Zaragoza (orcid)0000-0003-3057-6273 Rana, Omer F. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 19, 3 (2019), 32 [21 pp.] ACM Transactions on Internet Technology ACM Transactions on Internet Technology 1533-5399 1230194 http://zaguan.unizar.es/record/131364/files/texto_completo.pdf Postprint 1919433 http://zaguan.unizar.es/record/131364/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:131364 articulos driver 2024-02-08-10:06:05 ARTICLE