The Epigenome workflow is CPU bound because it spends 99 per cent of its runtime in the CPU and only 1 per cent on I/O and other activities. Advances in Science and Technology International Journal of Engineering Research in Africa Advanced Engineering Forum Journal of Biomimetics, Biomaterials and Biomedical Engineering Materials Science. Where are the trade offs between efficiency and cost? 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. The Broadband workflow used four earthquake sources measured at five sites and is memory limited because more than 75 per cent of its runtime is consumed by tasks requiring more than 1 GB of physical memory. The c1.xlarge type is nearly equivalent to abe.local and delivered nearly equivalent performance (within 8%), which indicates that the virtualization overhead does not seriously degrade performance. The processing costs for the Montage, Broadband and Epigenome workflows for the Amazon EC2 processors. A number of groups are adopting rigorous approaches to studying how applications perform on these new technologies. Under AmEC2's current cost structure, long-term storage of data is prohibitively expensive. Nevertheless, the cloud is clearly a powerful and cost-effective tool for CPU- and memory-bound applications, especially, if one-time, bulk processing is required and especially if data volumes involved are modest.  have shown that these data storage costs are, in the long term, much higher than would be incurred if the data were hosted locally. Epigenome's performance suggests that virtualization overhead may be more significant for a CPU-bound application: the processing time for c1.xlarge was some 10 per cent larger than for abe.local. For a memory-bound application such as Broadband, the processing advantage of the parallel file system disappears: abe.lustre offers only slightly better performance than abe.local. We have also compared the performance of academic and commercial clouds when executing the Kepler workflow. Input data were stored for the long term on elastic block store (EBS) volumes, but transferred to local disks for processing. We will refer to these instances by their AmEC2 name throughout the paper. Table 4.Summary of processing resources on the Abe high-performance cluster. Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Archives of the future must instead offer processing and analysis of massive volumes of data on distributed high-performance technologies and platforms, such as grids and the cloud. In general, the storage systems that produced the best workflow runtimes resulted in the lowest cost. Astronomers generally lack the training to perform system administration and job management tasks themselves; so there is a clear need for tools that will simplify these processes on their behalf. Wrangler users describe their deployments using a simple extensible markup language (XML) format, which specifies the type and quantity of VMs to provision, the dependencies between the VMs and the configuration settings to apply to each VM. 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. One group  is investigating the applicability of GPUs in astronomy by studying performance improvements for many types of applications, including input/output (I/O) and compute-intensive applications. It helps access the information using the cloud application. Tables 2 and 6 show the transfer sizes and costs for the three workflows. Table 2 includes the input and output data sizes. — Task manager (Condor Schedd): manages individual workflow tasks, supervising their execution on local and remote resources. Mobility . Variation with the number of cores of the runtime and data-sharing costs for the Epigenome workflow for the data storage options identified in table 7. G.B.B. An Investigation on Applications of Cloud Computing in Scientific Computing. Table 4.Summary of processing resources on the Abe high-performance cluster.  for descriptions and references. Table 7.File systems investigated on Amazon EC2. — Execution engine (DAGMan): executes the tasks defined by the workflow in order of their dependencies. Table 8.Performance and costs associated with the execution of periodograms of the Kepler datasets on Amazon and the NSF TeraGrid. Variation with the number of cores of the runtime and data-sharing costs for the Epigenome workflow for the data storage options identified in table 7. As a rule, cloud providers make available to end users root access to instances of virtual machines (VMs) running an operating system of the user's choice, but they offer no system administration support beyond ensuring that the VM instances function. It has double the memory of the other machine types, and the extra memory is used by the Linux kernel for the file system buffer cache to reduce the amount of time the application spends waiting for I/O. — AmEC2 offers no cost benefits over locally hosted storage, and is generally more expensive, but eliminates local maintenance and energy costs, and offers high-quality storage products. The Broadband workflow used four earthquake sources measured at five sites and is memory limited because more than 75 per cent of its runtime is consumed by tasks requiring more than 1 GB of physical memory. Table 7.File systems investigated on Amazon EC2. See Deelman. — The resources offered by AmEC2 are generally less powerful than those available in HPCs and generally do not offer the same performance. The GlusterFS deployments handle this type of workflow more efficiently. Wrangler then provisions and configures the VMs according to their dependencies, and monitors them until they are no longer needed. Comparison of workflow resource usage by application. — End users should understand the resource usage of their applications and undertake a cost–benefit study of cloud resources to establish a usage strategy. Here, we summarize the important results and the experimental details needed to properly interpret them. Briefly, Pegasus requires only that the end user supply an abstract description of the workflow, which consists simply of a directed acyclic graph (DAG) that represents the processing flow and the dependencies between tasks and then takes on the responsibility of managing and submitting jobs to the execution sites. Data transfer sizes per workflow on Amazon EC2. 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 . This advantage essentially disappears for CPU- and memory-bound applications. Table 8.Performance and costs associated with the execution of periodograms of the Kepler datasets on Amazon and the NSF TeraGrid. Montage generated an 8° square mosaic of the Galactic nebula M16 composed of images from the two micron all sky survey (2MASS) (http://www.ipac.caltech.edu/2mass/); the workflow is considered I/O-bound because it spends more than 95 per cent of its time waiting for I/O operations. FutureGrid available Nimbus and Eucalyptus cores in November 2010. Our goal was to understand which types of workflow applications run most efficiently and economically on a commercial cloud. Montage (http://montage.ipac.caltech.edu) aggregates into mosaics astronomical images in the flexible image transport system format, the international image format standards used in astronomy. Theme Issue ‘e-Science–towards the cloud: infrastructures, applications and research’ compiled and edited by Paul Townend, Jie Xu and Jim Austin, The application of cloud computing to scientific workflows: a study of cost and performance. The other is that Pegasus manages data on behalf of the user: infers the required data transfers, registers data into catalogues and captures performance information while maintaining a common user interface for workflow submission. The José Vasconcelos Library in Mexico City, Mexico, includes some … The Mapper can also restructure the workflow to optimize performance and adds transformations for data management and provenance information generation. 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. Such volumes mandate the development of a new computing model that will replace the current practice of mining data from electronic archives and data centres and transferring them to desktops for integration. Abstract: Cloud computing is a new concept emerged in the IT sector in recent years. Performance and costs associated with the execution of periodograms of the Kepler datasets on Amazon and the NSF TeraGrid. The book begins with an overview of cloud models supplied by the National Institute of Standards and Technology (NIST), and then: ), Figure 4. ), Figure 5. The cost on running this workflow on Amazon is approximately US$31, with US$2 in data transfer costs. Similarly, S3 is at a disadvantage, especially for workflows with many files, because Amazon charges a fee per S3 transaction. The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Runtimes in this context refer to the total amount of wall clock time in seconds from the moment the first workflow task is submitted until the last task completes. A submit host operating outside the cloud, at ISI, was used to host the workflow-management system and to coordinate all workflow jobs, and on AmEC2 all software was installed on two VM images, one for 32 bit instances and one for 64 bit instances. We estimated that a 448 h run of the Kepler analysis application on AmEC2 would cost over US$5000. Both PVFS and S3 performed poorly on workflows with a large number of small files, although the version of PVFS we used did not contain optimizations for small files that were included in subsequent releases. Pegasus offers two major benefits in performing the studies itemized in the introduction. The fixed monthly cost of storing input data for the three applications is shown in table 5. They are already common in astronomy, and will assume greater importance as research in the field becomes yet more data driven. Figure 1. These periodograms executed the Plavchan algorithm , the most computationally intensive algorithm implemented by the periodogram code. Transfer cost. Epigenome (http://epigenome.usc.edu/) maps short DNA segments collected using high-throughput gene sequencing machines to a previously constructed reference genome. The legend identifies the processor instances listed in tables 3 and 4. 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. By contrast, Epigenome shows much less variation than Montage because it is strongly CPU bound. For two of the applications (Montage, I/O intensive; Epigenome, CPU intensive), the lowest cost was achieved with GlusterFS, and for the other application, Broadband (memory intensive), the lowest cost was achieved with S3. Cloud computing offers a more flexible alternative than traditional HPC installations, particularly for scientists and researchers who have varied workloads or that require computing resources to scale with their workloads. While academic clouds cannot yet offer the range of services offered by AmEC2, their performance on the one product generated so far is comparable to that of AmEC2, and when these clouds are fully developed, may offer an excellent alternative to commercial clouds. Both instances use a 10 gigabits per second (Gbps) InfiniBand network. Both S3 and EBS have fixed monthly charges for the storage of data, and charges for accessing the data; these vary according to the application. Impact of this business model on end users should understand the resource cost the... Our implementation of the publicly released Kepler datasets and will assume greater importance as research in the executable to!, possibly owing to the cloud resources model are urgently needed relatively large of! Of academic and commercial clouds it allows one to scale up rapidly with disruptive technologies and Space Administration 's Archive. 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