These approaches usually need users to describe a topology for a deployed application. Cloud computing is a new way of purchasing computing and storage resources on demand through virtualization technologies. 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. Broadband generates a large number of small files, and this is why PVFS most likely performs poorly. With the rapid emergence of software systems and their applicability, the volume of users are growing exponentially. 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. In particular, we used the FutureGrid and Magellan academic clouds. Providers generally charge for all operations, including processing, transfer of input data into the cloud and transfer of data out of the cloud, storage of data, disk operations and storage of VM images and applications. should be automatically deployed on these resources. Table 1 summarizes the resource usage of each, rated as high, medium or low. The glide-ins contact a Condor central manager controlled by the user where they can be used to execute the user's jobs on the remote resources. S3 performs relatively well because the workflow reuses many files, and this improves the effectiveness of the S3 client cache.  for descriptions and references. Figure 1. Summary of processing resources on Amazon EC2. If there is less, some cores must sit idle to prevent the system from running out of memory or swapping. Resource cost. Table 10.Performance of periodograms on three different clouds. The scientific goal for our experiments was to calculate an atlas of periodograms for the time-series datasets released by the Kepler mission (http://kepler.nasa.gov/), which uses high-precision photometry to search for exoplanets transiting stars in a 105° square area in Cygnus. — Task manager (Condor Schedd): manages individual workflow tasks, supervising their execution on local and remote resources. We have also compared the performance of academic and commercial clouds when executing the Kepler workflow. Comparison of workflow resource usage by application. It supports VM-based environments, as well as native operating systems for experiments aimed at minimizing overheads and maximizing performance. NFS performed surprisingly well in cases where there were either few clients, or when the I/O requirements of the application were low. See Deelman et al. Figure 2 shows the resource cost for the workflows whose performances were given in figure 1. Tables 2 and 6 show the transfer sizes and costs for the three workflows. Table 6 summarizes the input and output sizes and costs. Table 5.Monthly storage cost for three workflows. See Deelman et al. Figure 1 compares the runtimes of the Montage, Broadband and Epigenome workflows on all the Amazon EC2 and Abe platforms listed in tables 3 and 4. Cloud Computing with e-Science Applications: Terzo, Olivier, Mossucca, Lorenzo: Amazon.sg: Books September 2011; Communications in Computer and Information Science 235:201-206; DOI: 10.1007/978-3 … Enter your email address below and we will send you the reset instructions. NFS was at a disadvantage compared with the other systems because it used an extra, dedicated node to host the file system; overloading a compute node to run the NFS server did not significantly reduce the cost. Footnotes. In detail, the goals of the study were to: — understand the performance of three workflow applications with different I/O, memory and CPU usage on a commercial cloud; — compare the performance of the cloud with that of a high-performance cluster equipped with a high-performance network and a parallel file system; and. S3 produced good performance for one application, possibly owing to the use of caching in our implementation of the S3 client. 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. For Broadband, the picture is quite different. 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. Runs 1 and 2 used two computationally similar algorithms, whereas Run 3 used an algorithm that was considerably more computationally intensive than those used in Runs 1 and 2. Project participants integrate existing open-source software packages to create an easy-to-use software environment that supports the instantiation, execution and recording of grid and cloud computing experiments. Such costs are excluded from the results presented here, which took advantage of applications designed for portability across multiple platforms. 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