Experiences with distributed computing for meteorological applications: grid computing and cloud computing

Abstract. Experiences with three practical meteorological applications with different characteristics are used to highlight the core computer science aspects and applicability of distributed computing to meteorology. Through presenting cloud and grid computing this paper shows use case scenarios fitting a wide range of meteorological applications from operational to research studies. The paper concludes that distributed computing complements and extends existing high performance computing concepts and allows for simple, powerful and cost-effective access to computing capacity.

[1]  Jens Volkert.  The Austrian Grid Initiative - High Level Extensions to Grid Middleware , 2004, PVM/MPI.

[2]  Antonio S. Cofiño,et al.  WRF4SG: A Scientific Gateway for climate experiment workflows , 2013 .

[3]  Rabiah Ahmad,et al.  Communications in Computer and Information Science , 2010 .

[4]  Ronald B. Smith,et al.  A Linear Theory of Orographic Precipitation , 2004 .

[5]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[6]  Constantinos Evangelinos,et al.  Cloud Computing for parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere- , 2008 .

[7]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[8]  Dengguo Feng,et al.  Study on Cloud Computing Security: Study on Cloud Computing Security , 2011 .

[9]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[10]  Ewa Deelman,et al.  The application of cloud computing to scientific workflows: a study of cost and performance , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Idar Barstad,et al.  An Extension of Smith’s Linear Theory of Orographic Precipitation: Introduction of Vertical Layers* , 2011 .

[12]  Alexandru Iosup,et al.  Workflow Monitoring and Analysis Tool for ASKALON , 2008, CoreGRID Workshop on Grid Middleware.

[13]  Radu Prodan,et al.  Impact of Variable Priced Cloud Resources on Scientific Workflow Scheduling , 2012, Euro-Par.

[14]  José Manuel Gutiérrez,et al.  Benefits and requirements of grid computing for climate applications. An example with the community atmospheric model , 2011, Environ. Model. Softw..

[15]  Ewa Deelman,et al.  The cost of doing science on the cloud: the Montage example , 2008, HiPC 2008.

[16]  R. V. van Nieuwpoort,et al.  The Grid 2: Blueprint for a New Computing Infrastructure , 2003 .

[17]  Daniele Catteddu and Giles Hogben.  Cloud Computing. Benefits, risks and recommendations for information security , 2009 .

[18]  Fumito Ito,et al.  Current Status and Future Directions , 2013 .

[19]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[21]  H. Raghav Rao,et al.  Security in grid computing: A review and synthesis , 2008, Decis. Support Syst..

[22]  Thomas Fahringer,et al.  Predicting the execution time of grid workflow applications through local learning , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[23]  Radu Prodan,et al.  Optimizing Performance of Automatic Training Phase for Application Performance Prediction in the Grid , 2007, HPCC.

[24]  Miron Livny,et al.  The cost of doing science on the cloud: The Montage example , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[25]  Amip J. Shah,et al.  Cost Model for Planning, Development and Operation of a Data Center , 2005 .

[26]  David E. Bernholdt,et al.  The earth system grid: enabling access to multimodel climate simulation data. , 2009 .

[27]  Craig H. Bishop,et al.  The THORPEX Interactive Grand Global Ensemble , 2010 .

[28]  Jun Qin,et al.  Towards a Light-weight Workflow Engine in the Asklon Grid Environment , 2007, CoreGRID.

[29]  Ladan Tahvildari,et al.  Cloud Computing Uncovered: A Research Landscape , 2012, Adv. Comput..

[30]  Moni Naor,et al.  Job Scheduling Strategies for Parallel Processing , 2017, Lecture Notes in Computer Science.

[31]  Dennis Gannon,et al.  Workflows for e-Science, Scientific Workflows for Grids , 2014 .

[32]  Sunita Rani,et al.  GRID COMPUTING AND CLOUD COMPUTING: DESCRIPTION AND COMPARISION , 2012 .

[33]  Jeffrey K. Hollingsworth.  Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2017, SC.

[34]  Frank Gens,et al.  Cloud Computing Benefits, risks and recommendations for information security , 2010 .

[35]  Johan Montagnat,et al.  Analyzing the EGEE Production Grid Workload: Application to Jobs Submission Optimization , 2009, JSSPP.

[36]  Radu Prodan,et al.  A New Fault Tolerance Heuristic for Scientific Workflows in Highly Distributed Environments Based on Resubmission Impact , 2009, 2009 Fifth IEEE International Conference on e-Science.

[37]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[38]  W. Cotton,et al.  RAMS 2001: Current status and future directions , 2003 .

[39]  Zhang Min,et al.  Study on Cloud Computing Security , 2011 .

[40]  Ian T. Foster,et al.  Automating Climate Science: Large Ensemble Simulations on the TeraGrid with the GriPhyN Virtual Data System , 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

[41]  Dimiter Syrakov,et al.  Grid computing for atmospheric composition studies in Bulgaria , 2010, Earth Sci. Informatics.

[42]  Rizos Sakellariou,et al.  An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm , 2003, Euro-Par.

[43]  Ian T. Foster,et al.  Data management and transfer in high-performance computational grid environments , 2002, Parallel Comput..

[44]  Yannis A. Dimitriadis,et al.  Grid Characteristics and Uses: A Grid Definition , 2003, European Across Grids Conference.