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Montage image engine is an astronomical tool created by NASA’s Earth Sciences Technology Office to obtain mosaics of the sky by the processing of multiple images from diverse regions. The associated computational processes involve the recalculation of the images geometry, the re-projection of the rotation and scale, the homogenization of the background emission and the combination of all images in a standardized format to show a final mosaic. These processes are highly computing demanding and structured in the form of workflows. A workflow is a set of individual jobs that allow the parallelization of the workload to be executed in distributed systems and thus, to reduce its finish time. Cloud computing is a distributed computing platform based on the provision of computing resources in the form of services becoming more and more required to perform large scale simulations in many science applications. Nevertheless, a computational cloud is a dynamic environment where resources capabilities can change on the fly depending on the networks demands. Therefore, flexible strategies to distribute workload among the different resources are necessary. In this work, the consideration of fuzzy rule-based systems as local brokers in cloud computing is proposed to speed up the execution of the Montage workflows. Simulations of the expert broker using synthetic workflows obtained from real systems considering diverse sets of jobs are conducted. Results show that the proposal is able to significantly reduce makespan in comparison to well-known scheduling strategies in distributed systems and in this way, to offer an efficient solution to accelerate the processing of astronomical image mosaic workflows.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
5--20
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
autor
- Telecommunication Engineering Department. University of Jaén. Alfonso X el Sabio, 28 Linares, Jaén. Spain.
autor
- Telecommunication Engineering Department. University of Jaén. Alfonso X el Sabio, 28 Linares, Jaén. Spain.
autor
- Telecommunication Engineering Department. University of Jaén. Alfonso X el Sabio, 28 Linares, Jaén. Spain.
autor
- Telecommunication Engineering Department. University of Jaén. Alfonso X el Sabio, 28 Linares, Jaén. Spain.
autor
- Telecommunication Engineering Department. University of Jaén. Alfonso X el Sabio, 28 Linares, Jaén. Spain.
Bibliografia
- [1] Belarbi, K., Titel, F., Bourebia, W., Benmahammed, K. (2005). Design of mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm, Engineering Applications of Artificial Intelligence, 18(7), 875–880
- [2] Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K. (2008). Characterization of scientific workflows, in Workflows in Support of Large-Scale Science, 2008. WORKS 2008. Third Workshop on, 1–10
- [3] Blythe, J., Jain, S., Deelman, E., Gil, Y., Vahi, K., Mandal, A., Kennedy, K. (2005). Task scheduling strategies for workflow-based applications in grids, in Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CC-Grid’ 05) - Volume 2 - Volume 02, CCGRID ’05, (Washington, DC, USA), IEEE Computer Society, 759–767
- [4] Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F. (2001). A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems, J. Parallel Distrib. Comput., 61, 810–837
- [5] Chen, W., Deelman, E. (2012). Workflowsim: A toolkit for simulating scientific workflows in distributed environments, in E-Science (e-Science), 2012 IEEE 8th International Conference on, 1–8
- [6] Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L. (2001). Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific Pub Co Inc
- [7] Cordón, O., Herrera, F., Villar, P. (2001). Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base, Fuzzy Systems, IEEE Transactions on, 9, 667–674
- [8] Deelman, E., Singh, G., Livny, M., Berriman, B., Good, J. (2008). The cost of doing science on the cloud: The montage example, in Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, SC ’08, (Piscataway, NJ, USA), IEEE Press, 50:1–50:12
- [9] Deelman, E., Singh, G., Su, M., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Berriman, G., Good, J., Laity, A., Jacob, J., Katz, D. (2005). Pegasus: a framework for mapping complex scientific workflows onto distributed systems, J. Sci. Programm., 13(2), 219–237
- [10] FutureGrid, https://portal.futuregrid.org/
- [11] García-Galán, S., Prado, R.P., Muńoz-Expósito, J.E. (2012). Fuzzy scheduling with swarm intelligence-based knowledge acquisition for grid computing, Engineering Applications of Artificial Intelligence, 25(2), 359–375
- [12] IPAC, Infrared processing and analysis center, http://www.ipac.caltech.edu/
- [13] IPAC, The two micron all sky survey, http://www.ipac.caltech.edu/2mass
- [14] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K. (2013). Characterizing and profiling scientific workflows, Future Generation Computer Systems Special Section: Recent Developments in High Performance Computing and Security, 29(3), 682–692
- [15] Mamdani, E.H., et al. (1974). Application of fuzzy algorithms for control of simple dynamic plant, Procedings of IEEE, 121(12), 1585–1588
- [16] Montage, http://montage.ipac.caltech.edu
- [17] Pegasus, https://confluence.pegasus.isi.edu/display/pegasus/workflowgenerator
- [18] Prado, R.P., Hoffmann, F., García-Galán, S., Muńoz-Expósito, J.E., Bertram, T. (2012). On providing quality of service in grid computing through multi-objective swarm-based knowledge acquisition in fuzzy schedulers, Int. J. Approx. Reasoning, 53, 228–247
- [19] Prathibha, S. (2013). Monitoring the performance analysis of executing workflow applications with different resource types in a cloud environment, in 1st International Symposium on Big Data and Cloud Computing Challenges(ISBCC-2014), (VIT University, Chennai, India)
- [20] SDSS, Sloan digital sky survey, http://www.sdss.org/
- [21] STScI, http://www.stsci.edu/resources/
- [22] Xhafa, F., Abraham, A. (2010). Computational models and heuristic methods for grid scheduling problems, Future Generation Computer Systems, 26(4), 608–621
- [23] Xhafa, F., Abraham, A. (2008). Meta-heuristics for grid scheduling problems, Metaheuristics for Scheduling: Distributed Computing Environments, Studies in Computational Intelligence, Springer Verlag, Germany, ISBN, 978–3
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6a8756ae-403d-40b6-99bf-335a52e8e843
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