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scientific applications of cloud computing

This is particularly the case for I/O-bound applications, whose performance benefits greatly from the availability of parallel file systems. We measured and compared the total execution time of the workflows on these resources, their input/output needs and quantified the costs. This study describes investigations of the applicability of cloud computing to scientific workflow applications, with emphasis on astronomy. We executed two sets of relatively small processing runs on the Amazon cloud, and a larger run on the TeraGrid, a large-scale US Cyberinfrastructure. The rates for fixed charges are US$0.15 per GB month for S3, and US$0.10 per GB month for EBS. Epigenome (http://epigenome.usc.edu/) maps short DNA segments collected using high-throughput gene sequencing machines to a previously constructed reference genome. Variation with the number of cores of the runtime and data-sharing costs for the Broadband workflow for the data storage options identified in table 7. While data transfer costs for Epigenome and Broadband are small, for Montage, they are larger than the processing and storage costs using the most cost-effective resource type. As before, we used Pegasus to manage the workflow and Wrangler to manage the cloud resources. (Online version in colour. Amazon S3 performs poorly because of the relatively large overhead of fetching the many small files that are produced by these workflows. Data transfer sizes per workflow on Amazon EC2. AmEC2 is the most popular, feature-rich and stable commercial cloud, and Abe, decommissioned since these experiments, is typical of high-performance computing (HPC) systems, as it is equipped with a high-speed network and a parallel file system to provide high-performance I/O. We have also compared the performance of academic and commercial clouds when executing the Kepler workflow. Table 10.Performance of periodograms on three different clouds. Both Canon et al. The variable charges are US$0.01 per 1000 PUT operations and US$0.01 per 10 000 GET operations for S3, and US$0.10 per million I/O operations for EBS. The left-hand panels in figure 3 through to figure 5 show how the three workflows performed with these file systems, as the number of worker nodes increased from 1 to 8. Data Storage and Backup. IU, Indiana University; UofC, University of Chicago; UCSD, University of California San Diego; UFI, University of Florida. Figure 1. [10] for descriptions and references. A thorough cost–benefit analysis, of the kind described here, should always be carried out in deciding whether to use a commercial cloud for running workflow applications, and end-users should perform this analysis every time price changes are announced. NFS performed surprisingly well in cases where there were either few clients, or when the I/O requirements of the application were low. They exclude the times for starting the VMs (typically, 70–90 s), data transfer time and queue delays for starting glide-in jobs on Abe. NFS performed surprisingly well in cases where there were either few clients, or when the I/O requirements of the application were low. The 32 bit image used for the experiments in this study was 773 MB, compressed, and the 64 bit image was 729 MB, compressed, for a total fixed cost of US$0.22 per month. The investigations described above used the AmEC2 EBS storage system, but data were transferred to local disks to run the workflows. The astronomical community is collaborating with computer scientists in investigating how emerging technologies can support the next generation of what has come to be called data-driven astronomical computing [2]. Summary of processing resources on the Abe high-performance cluster. Cloud computing can be used more easily and quickly in financial applications, adjusting resource being used by accounting software dynamically, reducing overall investment in accounting modernization, improving the utilization and the effect of IT equipment. Table 8 shows the results of processing 210 000 Kepler time-series datasets on AmEC2 using 128 cores (16 nodes) of the c1.xlarge instance type (Runs 1 and 2) and of processing the same datasets on the NSF TeraGrid using 128 cores (8 nodes) from the Ranger cluster (Run 3). Another group [4] has shown how MapReduce and Hadoop [5] can support parallel processing of the images released by the Sloan Digital Sky Survey (http://wise.sdss.org/). Table 4.Summary of processing resources on the Abe high-performance cluster. While the costs will change with time, this paper shows that the study must account for itemized charges for resource usage, data transfer and storage. The differences in performance are reflected in the costs of running the workflows, shown in the right-hand panels of figure 3 through to figure 5. A number of such tools are under development, and the investigations reported here used two of them: Wrangler [9] and the Pegasus Workflow Management System [10]. Table 6 summarizes the input and output sizes and costs. Wrangler, as mentioned above, allows the user to specify the number and type of resources to provision from a cloud provider and to specify what services (file systems, job schedulers, etc.) Given that scientists will almost certainly need to transfer products out of the cloud, transfer costs may prove prohibitively expensive for high-volume products. One example is Magellan, deployed at the US Department of Energy's National Energy Research Scientific Computing Center with Eucalyptus technologies (http://open.eucalyptus.com/), which are aimed at creating private clouds. We will refer to these instances by their AmEC2 name throughout the paper. DAGMan relies on the resources (compute, storage and network) defined in the executable workflow to perform the necessary actions. We created a single workflow for each application to be used throughout the study. FutureGrid available Nimbus and Eucalyptus cores in November 2010. It’s been a huge advantage to be part of the AWS network and leverage all of those relationships and technologies. Broadband generates a large number of small files, and this is why PVFS most likely performs poorly. The book provides the scientific community with an essential reference for moving applications to the cloud. is supported by the NASA Exoplanet Science Institute at the Infrared Processing and Analysis Center, operated by the California Institute of Technology in coordination with the Jet Propulsion Laboratory (JPL). While the AmEC2 instances are not prohibitively slow, the processing times on abe.lustre are nevertheless nearly three times faster than the fastest AmEC2 machines. Reasonably good performance was achieved on all instances except m1.small, which is much less powerful than the other AmEC2 resource types. Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username, Infrared Processing and Analysis Center, Caltech, Pasadena, CA 91125, USA, University of Southern California Information Sciences Institute, Marina del Rey CA 90292, USA. The architecture of the cloud is well suited to this type of application, whereas tightly coupled applications, where tasks communicate directly via an internal high-performance network, are most likely better suited to processing on computational grids [6]. In addition to Amazon S3, which the vendor maintains, common file systems such as the network file system (NFS), GlusterFS and the parallel virtual file system (PVFS), can be deployed on AmEC2 as part of a virtual cluster, with configuration tools such as Wrangler, which allows clients to coordinate launches of large virtual clusters. Abe.local's performance is only 1 per cent better than c1.xlarge; so virtualization overhead is essentially negligible. See Deelman. We ran experiments on AmEC2 (http://aws.amazon.com/ec2/) and the National Center for Supercomputer Applications Abe high-performance cluster (http://www.ncsa.illinois.edu/UserInfo/Resources/Hardware/Intel64Cluster/). Table 2 includes the input and output data sizes. We report here the results of investigations of the applicability of commercial cloud computing to scientific computing, with an emphasis on astronomy, including investigations of what types of applications can be run cheaply and efficiently on the cloud, and an example of an application well suited to the cloud: processing a large dataset to create a new science product. Workflow applications are data-driven, often parallel, applications that use files to communicate data between tasks. We executed two sets of relatively small processing runs on the Amazon cloud, and a larger run on the TeraGrid, a large-scale US Cyberinfrastructure. S3 performs relatively well because the workflow reuses many files, and this improves the effectiveness of the S3 client cache. Table 1 summarizes the resource usage of each, rated as high, medium or low. Abstract: Cloud computing is a new concept emerged in the IT sector in recent years. United States Department of Energy Advanced Scientific Computing Research (ASCR) Program. They improve the performance of workflow applications by reducing some of the wide-area system overheads. Another example of an academic cloud is the FutureGrid testbed (https://portal.futuregrid.org/about), designed to investigate computer science challenges related to the cloud computing systems such as authentication and authorization, interface design, as well as the optimization of grid- and cloud-enabled scientific applications [13]. It helps access the information using the cloud application. In addition, there were 4616 GET operations and 2560 PUT operations for a total variable cost of approximately US$0.03. Among the questions that require investigation are: what kinds of applications run efficiently and cheaply on what platforms? This work was supported in part by the National Science Foundation under grants nos 0910812 (FutureGrid) and OCI-0943725 (CorralWMS). Cloud computing, method of running application software and storing related data in central computer systems and providing customers or other users access to them through the Internet. While the costs will change with time, this paper shows that the study must account for itemized charges for resource usage, data transfer and storage. Table 9.FutureGrid available Nimbus and Eucalyptus cores in November 2010. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems. Because AmEC2 can be prohibitively expensive for long-term processing and storage needs, we have made preliminary investigations of the applicability of academic clouds in astronomy, to determine in the first instance how their performance compares with those of commercial clouds. Broadband (memory bound). 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Here indicate how cloud computing environment is identified as NP-hard problem due to the use caching... Aimed at minimizing overheads and hidden costs in using these technologies wide-area system overheads Archive.

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