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In those systems, computing resources are used to accomplish many computational tasks in high performance environments, such as multiprocessor machines or clusters. Meanwhile, cloud computing provides scalable and elastic resources that can be instantiated on demand during the course of a scientific experiment, without requiring its users to acquire expensive infrastructure or to configure many pieces of software. In fact, because of these advantages some scientists have already adopted the cloud model in their scientific experiments. However, this model also raises many challenges. When scientists are executing scientific workflows that require parallelism, it is hard to decide a priori the amount of resources to use and how long they will be needed because the allocation of these resources is elastic and based on demand. In addition, scientists have to manage new aspects such as initialization of virtual machines and impact of data staging. SciCumulus is a middleware that manages the parallel execution of scientific workflows in cloud environments. In this paper, we introduce an adaptive approach for executing parallel scientific workflows in the cloud. This approach adapts itself according to the availability of resources during workflow execution. It checks the available computational power and dynamically tunes the workflow activity size to achieve better performance. Experimental evaluation showed the benefits of parallelizing scientific workflows using the adaptive approach of SciCumulus, which presented an increase of performance up to 47.1%. Copyright \u00a9 2011 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/cpe.1880","type":"journal-article","created":{"date-parts":[[2011,10,7]],"date-time":"2011-10-07T21:18:43Z","timestamp":1318022323000},"page":"1531-1550","source":"Crossref","is-referenced-by-count":32,"title":["An adaptive parallel execution strategy for cloud\u2010based scientific workflows"],"prefix":"10.1002","volume":"24","author":[{"given":"Daniel","family":"de Oliveira","sequence":"first","affiliation":[{"name":"COPPE\u2010UFRJ\/Federal University of Rio de Janeiro Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Ogasawara","sequence":"additional","affiliation":[{"name":"COPPE\u2010UFRJ\/Federal University of Rio de Janeiro Rio de Janeiro Brazil"},{"name":"CEFET\u2010RJ Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kary","family":"Oca\u00f1a","sequence":"additional","affiliation":[{"name":"COPPE\u2010UFRJ\/Federal University of Rio de Janeiro Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernanda","family":"Bai\u00e3o","sequence":"additional","affiliation":[{"name":"NP2Tec\u2010UNIRIO\/Federal University of the State of Rio de Janeiro Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marta","family":"Mattoso","sequence":"additional","affiliation":[{"name":"COPPE\u2010UFRJ\/Federal University of Rio de Janeiro Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2011,10,7]]},"reference":[{"key":"e_1_2_9_2_1","volume-title":"The Fourth Paradigm: Data\u2010Intensive Scientific Discovery","author":"Hey T","year":"2009"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1142\/7243"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","unstructured":"LinC LuS FeiX PaiD HuaJ.A Task Abstraction and Mapping Approach to the Shimming Problem in Scientific Workflows.Proceedings of the International Conference on Services Computing 2009 Bangalore India 2009;284\u2013291.","DOI":"10.1109\/SCC.2009.77"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-757-2"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJBPIM.2010.033176"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"WalkerE GuiangC.Challenges in executing large parameter sweep studies across widely distributed computing environments.Workshop on Challenges of large applications in distributed environments Monterey California USA 2007;11\u201318.","DOI":"10.1145\/1273404.1273411"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"RaicuI FosterI ZhaoY.Many\u2010task computing for grids and supercomputers.Workshop on Many\u2010Task Computing on Grids and Supercomputers Austin Texas 2008;1\u201311.","DOI":"10.1109\/MTAGS.2008.4777912"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1496091.1496100"},{"key":"e_1_2_9_10_1","article-title":"Twenty\u2010One Experts Define Cloud Computing","author":"Geelan J","year":"2009","journal-title":"Cloud Computing Journal"},{"key":"e_1_2_9_11_1","doi-asserted-by":"crossref","unstructured":"El\u2010KhamraY KimH JhaS ParasharM.Exploring the Performance Fluctuations of HPC Workloads on Clouds Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science Washington DC USA 2010;383\u2013387.","DOI":"10.1109\/CloudCom.2010.84"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"JacksonKR RamakrishnanL MurikiK CanonS CholiaS ShalfJ WassermanHJ WrightNJ.Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud.Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science Washington DC USA 2010;159\u2013168.","DOI":"10.1109\/CloudCom.2010.69"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"HeQ ZhouS KoblerB DuffyD McGlynnT.Case study for running HPC applications in public clouds Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing New York NY USA 2010;395\u2013401.","DOI":"10.1145\/1851476.1851535"},{"key":"e_1_2_9_14_1","unstructured":"Amazon EC2. 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