PPT-Parallel Applications And Tools For Cloud Computing Environ

SC 10 New Orleans USA Nov 17 2010 Azure MapReduce AzureMapReduce A MapRedue runtime for Microsoft Azure using Azure cloud services Azure Compute Azure BLOB storage for inoutintermediate data storage. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Dr. Peter DePasquale. The . College of New Jersey. Computer Science. Overview. Introduction to cloud computing. Introduction to Amazon Web Services (AWS). Hands-On portion. Work with your own virtual server (via my AWS account). Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . CloudCom. 2010. Indianapolis, Indiana, USA. Nov 30 – Dec 3, 2010. Azure . MapReduce. AzureMapReduce. A . MapRedue. runtime for Microsoft Azure using Azure cloud services. Azure Compute. Azure BLOB storage for in/out/intermediate data storage. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . June 14 2012. Cloud and Autonomic Computing Center Spring 2012 Workshop. Cloud . Computing: from Cybersecurity to . Intercloud. University of Florida Gainesville. Geoffrey Fox. gcf@indiana.edu. . on . Clouds. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . Prof.Geoffrey. . Fox (gcf. @indiana.edu). Committee . : . Prof.Judy. . Qiu. , . Prof.Beth. . Plale. , . Prof.David. . Leake. Supercomputers are tightly integrated computer systems, dedicated to running a specialized task. Quantum mechanics. Weather forecasting / climate research. Oil / gas exploration. Physical simulations etc.. 1. Table of contents:. 1. What is the Cloud?. 2. Cloud Computing Service Models.. - Software as a Service (SaaS).. - Platform as a Service (PaaS).. - Infrastructure as a Service (IaaS).. 3. Benefits of Cloud Computing.. Authors. : . Qiang. . Lu*, . You . Xu. †, . Ruoyun. Huang†, . Yixin. Chen† and . Guoliang. . Chen* from. *University . of Science and Technology of . China. †Washington . University in St. . Managing Federal Records in. Cloud Computing Environments. Arian D. Ravanbakhsh. Electronic Records Policy Specialist. RACO Chicago August 24, 2010. Outline. Context/Background re. Cloud Computing. Use scenarios. Learning Objectives. What is Cloud Computing?. Cloud Characteristics. Cloud Benefits. What is pay-as-you-go?. Economies of Scale. As-a-service models: IaaS, PaaS, SaaS. Cloud deployment models: private, public, and hybrid clouds. Prof. . Raffaele Montella, Ph.D.. http://raffaelemontella.it. raffaele.montella@uniparthenope.it. Outline - Computing in the Cloud. Introduction. Computing as a service. Virtualization and Containerization.