Registro:

Referencias:

• Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., Tsafrir, D., Deconstructing Amazon EC2 spot instance pricing (2013) ACM Transactions on Economics and Computation, pp. 161-1620
• (2016) EC2 Spot Instances, , Amazon:, Online; accessed September-2016
• Corradi, A., Fanelli, M., Foschini, L., VM consolidation: A real case based on OpenStack Cloud (2014) Future Generation Computer Systems, pp. 118-127
• Berriman, G.B., Deelman, E., Good, J.C., Jacob, J.C., Katz, D.S., Kesselman, C., Laity, A.C., Su, M.-H., (2004) Montage: A Grid-enabled Engine for Delivering Custom Science-grade Mosaics on demand, pp. 221-232
• Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K., Characterization of scientific workflows (2008) Workflows in Support of Large-Scale Science, 2008. WORKS 2008. Third Workshop on, pp. 1-10
• Brown, D.A., Brady, P.R., Dietz, A., Cao, J., Johnson, B., McNabb, J., (2007) A Case Study on the use of Workflow Technologies for Scientific Analysis: Gravitational Wave Data Analysis, , Springer London
• Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R., CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms (2011) Software: Practice and Experience, pp. 23-50
• Monge, D.A., Holec, M., Zelezny, F., Garcia Garino, C., Ensemble learning of runtime prediction models for gene-expression analysis workflows (2015) Cluster Computing, pp. 1-13
• Deelman, E., Mehta, G., Singh, G., Su, M.-H., Vahi, K., (2007) Pegasus: Mapping Large-Scale Workflows to Distributed Resources in Workflows for E-Science: Scientific Workflows for Grids, , Springer London
• Iosup, A., Yigitbasi, N., Epema, D., (2011) On the Performance Variability of Production Cloud Services, pp. 104-113
• Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K., Characterizing and profiling scientific workflows (2013) Future Generation Computer Systems, pp. 682-692
• Jonathan, Livny, T., High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs (2008) PLoSONE, pp. 1-12
• Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds (2015) Future Generation Computer Systems, pp. 1-18. , Special Section: Business and Industry Specific Cloud
• Maechling, P., Deelman, E., Zhao, L., Graves, R., Mehta, G., Gupta, N., Mehringer, J., Field, E., (2007) SCEC Cyber-Shake Workflows-Automating Probabilistic Seismic Hazard Analysis Calculations in Workflows for E-Science: Scientific Workflows for Grids, , Springer London
• Mann, H.B., Whitney, D.R., On a test of whether one of two random variables is stochastically larger than the other (1947) The Annals of Mathematical Statistics, pp. 50-60
• Mao, M., Humphrey, M., Auto-scaling to minimize cost and meet application deadlines in cloud workflows (2011) Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, p. 49
• Mao, M., Humphrey, M., Scaling and scheduling to maximize application performance within budget constraints in cloud workflows (2013) Parallel & Distributed Processing (IPDPS), 2013 IEEE 27th International Symposium on, pp. 67-78
• Zhu, M., Wu, Q., Zhao, Y., A cost-effective scheduling algorithm for scientific workflows in clouds (2012) Performance Computing and Communications Conference (IPCCC), 2012 IEEE 31st International, pp. 256-265
• Monge, D.A., Garcia Garino, C., (2014) Adaptive Spot-Instances Aware Autoscaling for Scientific Workflows on the Cloud in High Performance Computing, , Springer Berlin Heidelberg
• Poola, D., Kumar Garg, S., Buyya, R., Yang, Y., Ramamohanarao, K., Robust scheduling of scientific workflows with deadline and budget constraints in clouds (2014) 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pp. 858-865
• Poola, D., Ramamohanarao, K., Buyya, R., Fault-tolerant workflow scheduling using spot instances on clouds (2014) Procedia Computer Science, pp. 523-533
• Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I., Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility (2009) Future Generation Computer Systems, pp. 599-616
• Rahman, M., Hassan, R., Ranjan, R., Buyya, R., Adaptive workflow scheduling for dynamic grid and cloud computing environment (2013) Concurrency and Computation: Practice and Experience, pp. 1816-1842
• Pllana, S., Brandic, I., Benkner, S., A survey of the state of the art in performance modeling and prediction of parallel and distributed computing systems (2008) International Journal of Computational Intelligence Research, pp. 279-284
• Schad, J., Dittrich, J., Quiand-Ruiz, J.-A., Runtime measurements in the cloud: Observing, analyzing, and reducing variance (2010) Proc. VLDB Endow., pp. 460-471
• Turchenko, V., Shultz, V., Turchenko, I., Wallace, R.M., Sheikhalishahi, M., Luis Vazquez-Poletti, J., Lucio, G., Spot price prediction for cloud computing using neural networks (2013) International Journal of Computing, pp. 348-359
• Voorsluys, W., Buyya, R., Reliable provisioning of spot instances for compute-intensive applications (2012) Advanced Information Networking and Applications (AINA), 2012 IEEE 26th International Conference on, pp. 542-549
• Wallace, R.M., Turchenko, V., Sheikhalishahi, M., Turchenko, I., Shults, V., Vazquez-Poletti, J.L., Grandinetti, L., Applications of neural-based spot market prediction for cloud computing (2013) Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2013 IEEE 7th International Conference on, pp. 710-716
• Chi Zhou, A., He, B., Liu, C., Monetary cost optimizations for hosting workflow-as-a-service in IaaS clouds (2014) IEEE Transactions on Cloud Computing, pp. 34-48

Citas:

---------- APA ----------

---------- CHICAGO ----------

---------- MLA ----------

---------- VANCOUVER ----------