scientific applications of cloud computing

Glide-ins are a scheduling technique where Condor workers are submitted as user jobs via grid protocols to a remote cluster. Allowing us to produce a browser-based solution that can be accessed on any device is a game-changer for the scientific community. They are, however, computationally expensive, but easy to parallelize because the processing of each frequency is performed independently of all other frequencies. Epigenome (CPU bound). However, when computations grow larger, the costs of computing become significant. Column 1 of table 3 lists five AmEC2 compute resources (‘types’) chosen to reflect the range of resources offered. Variation with the number of cores of the runtime and data-sharing costs for the Montage workflow for the data storage options identified in table 7. Clouds are under development in academia to evaluate technologies and support research in the area of on-demand computing. The potential applications of cloud computing are many: financial applications, health care services, business enterprises and many others. 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. Amazon S3 performs poorly because of the relatively large overhead of fetching the many small files that are produced by these workflows. The role of cloud computing on a corporate level can be either for the in house operations, or as a deployment tool for software or services the company develops for the public. See Deelman et al. Figure 2. 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. Table 4.Summary of processing resources on the Abe high-performance cluster. One group [3] 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. Figure 1. Table 2.Data transfer sizes per workflow on Amazon EC2. The fixed monthly cost of storing input data for the three applications is shown in table 5. 1 AmEC2 no longer charges for data transfer into its cloud. Broadband generates a large number of small files, and this is why PVFS most likely performs poorly. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. In general, GlusterFS delivered good performance for all the applications tested and seemed to perform well with both a large number of small files, and a large number of clients. IU, Indiana University; UofC, University of Chicago; UCSD, University of California San Diego; UFI, University of Florida. 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. 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 Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Broadband (memory bound). Monthly storage cost for three workflows. Wrangler then provisions and configures the VMs according to their dependencies, and monitors them until they are no longer needed. should be automatically deployed on these resources. We can see that the performance on the three clouds is comparable, achieving a speed up of approximately 43 on 48 cores. should be automatically deployed on these resources. Figure 2 shows the resource cost for the workflows whose performances were given in figure 1. 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. S3 produced good performance for one application, possibly owing to the use of caching in our implementation of the S3 client. One is that it allows applications to be automatically executed on different execution sites, under the assumption that they are written for portability, with no special coding needed to support different compute platforms. DAGMan relies on the resources (compute, storage and network) defined in the executable workflow to perform the necessary actions. To better support the growing use of cloud computing resources with increasing data- and compute-intensive research and education workloads, the National Science Foundation’s (NSF) Directorate for Computer and Information Science and Engineering announced the Cloud Access solicitation in September 2018. 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 costs of transferring data into and out of the Amazon EC2 cloud. Among the questions that require investigation are: what kinds of applications run efficiently and cheaply on what platforms? The runtimes in hours for the Montage, Broadband and Epigenome workflows on the Amazon EC2 cloud and on Abe. PVFS likely performs poorly because the small file optimization that is part of the current release had not been incorporated at the time of the experiment. The performances of the different workflows do, however, depend on the architectures of the storage system used, and on the way in which the workflow application itself uses and stores files, both of which of course govern how efficiently data are communicated between workflow tasks. Two publications [7,8] detail the impact of this business model on end users of commercial and academic clouds. 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. Table 10 shows the characteristics of the various cloud deployments and the results of the computations. Reasonably good performance was achieved on all instances except m1.small, which is much less powerful than the other AmEC2 resource types. In particular, academic clouds may provide an alternative to commercial clouds for large-scale processing. Table 1.Comparison of workflow resource usage by application. Wrangler then provisions and configures the VMs according to their dependencies, and monitors them until they are no longer needed. ), Figure 5. The results of these early experiments are highly encouraging. © 2012 The Author(s) Published by the Royal Society. DAGMan relies on the resources (compute, storage and network) defined in the executable workflow to perform the necessary actions. We provisioned 48 cores each on Amazon EC2, FutureGrid and Magellan, and used the resources to compute periodograms for 33 000 Kepler datasets. In addition, there were 3.18 million I/O operations for a total variable cost of US$0.30. Full technical experimental details are given in recent studies [6,11]. File systems investigated on Amazon EC2. Data Storage and Backup. Enter your email address below and we will send you the reset instructions. 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). 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. 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. We provisioned 48 cores each on Amazon EC2, FutureGrid and Magellan, and used the resources to compute periodograms for 33 000 Kepler datasets. [10] for descriptions and references. Such a study is, however, a major undertaking and outside the scope of this paper. The project has already released nearly 400 000 time-series datasets, and this number will grow considerably by the end of the mission in 2014. — End users should understand the resource usage of their applications and undertake a cost–benefit study of cloud resources to establish a usage strategy. Epigenome (http://epigenome.usc.edu/) maps short DNA segments collected using high-throughput gene sequencing machines to a previously constructed reference genome. All application executables and input files were stored in the Lustre file system. Cloud Computing with e-Science Applications explains how cloud computing can improve data management in data-heavy fields such as bioinformatics, earth science, and computer science. Given that scientists will almost certainly need to transfer products out of the cloud, transfer costs may prove prohibitively expensive for high-volume products. 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. 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/). 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. The walltime measures the end-to-end workflow execution, while the cumulative duration is the sum of the execution times of all the tasks in the workflow. 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. The rates for fixed charges are US$0.15 per GB month for S3, and US$0.10 per GB month for EBS. Comparison of workflow resource usage by application. The cloud resources were configured as a Condor pool using the Wrangler provisioning and configuration tool [14]. They cite the example of hosting the 12 TB volume of the 2MASS survey, which would cost US$12 000 per year if stored on S3, the same cost as the outright purchase of a disk farm, inclusive of hardware purchase, support and facility and energy costs for 3 years. The figure clearly shows the trade off between performance and cost for Montage. — Execution engine (DAGMan): executes the tasks defined by the workflow in order of their dependencies. The most important result of figure 1 is a demonstration of the performance advantage of high-performance parallel file systems for an I/O-bound application. The rates for fixed charges are US$0.15 per GB month for S3, and US$0.10 per GB month for EBS. The figure clearly shows the trade off between performance and cost for Montage. 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. Processing costs do not vary widely with machine, so there is no reason to choose anything other than the most powerful machines. We used the Eucalyptus and Nimbus technologies to manage and configure resources, and to constrain our resource usage to roughly a quarter of the available resources in order to leave resources available for other users. 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. 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. — Virtualization overhead on AmEC2 is generally small, but most evident for CPU-bound applications. 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. 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. Cloud computing is a new way of purchasing computing and storage resources on demand through virtualization technologies. A few approaches try to use the topology information to improve the performance of systems (e.g., in [7]). Reasonably good performance was achieved on all instances except m1.small, which is much less powerful than the other AmEC2 resource types. — End users should understand the resource usage of their applications and undertake a cost–benefit study of cloud resources to establish a usage strategy. Where are the trade offs between efficiency and cost? Evaluations of how new technologies such as cloud computing would support such a new distributed computing model are urgently needed. 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. The result shows that for relatively small computations, commercial clouds provide good performance at a reasonable cost. This study describes investigations of the applicability of cloud computing to scientific workflow applications, with emphasis on astronomy. ), Figure 4. 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. Performance of periodograms on three different clouds. On Abe, Globus (http://www.globus.org/) and Corral [12] were used to deploy Condor glide-in jobs that started Condor daemons on the Abe worker nodes, which in turn contacted the submit host and were used to execute workflow tasks. Traditional grids and clusters use network or parallel file systems. Everything can be implemented in the cloud service: from data storage to data analysis, applications of any scale or size. The investigations described above used the AmEC2 EBS storage system, but data were transferred to local disks to run the workflows. Cloud Computing with e-Science Applications: Terzo, Olivier, Mossucca, Lorenzo: Amazon.sg: Books The book provides the scientific community with an essential reference for moving applications to the cloud. 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. Processing will instead often take place on high-performance servers co-located with data. We created a single workflow for each application to be used throughout the study. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. The use of Amazon EC2 resources were supported by the AWS in Education research grant. Enter your email address below and we will send you the reset instructions. The commodity AmEC2 hardware evaluated here cannot match the performance of HPC systems for I/O-bound applications, but as AmEC2 offers more high-performance options, their cost and performance should be investigated. 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. On Abe, Globus (http://www.globus.org/) and Corral [12] were used to deploy Condor glide-in jobs that started Condor daemons on the Abe worker nodes, which in turn contacted the submit host and were used to execute workflow tasks. TABLE OF … This work was supported in part by the National Science Foundation under grants nos 0910812 (FutureGrid) and OCI-0943725 (CorralWMS). The Mapper can also restructure the workflow to optimize performance and adds transformations for data management and provenance information generation. on the cloud. For abe.lustre, all intermediate and output data were written to the Lustre file system. Table 3.Summary of processing resources on Amazon EC2. ), Figure 4. PVFS likely performs poorly because the small file optimization that is part of the current release had not been incorporated at the time of the experiment. The GlusterFS deployments handle this type of workflow more efficiently. 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. In general, the storage systems that produced the best workflow runtimes resulted in the lowest cost. Broadband (http://scec.usc.edu/research/cme/) generates and compares synthetic seismograms for several sources (earthquake scenarios) and sites (geographical locations). One contribution of 13 to a Theme Issue ‘e-Science–towards the cloud: infrastructures, applications and research’. See Deelman et al. This is because m1.small has only a 50 per cent share of one core, and only one of the cores can be used on c1.medium because of memory limitations. The experiments summarized here indicate how cloud computing may play an important role in data-intensive astronomy, and presumably in other fields as well. The use of Amazon EC2 resources were supported by the AWS in Education research grant. 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 Amazon Elastic Compute Cloud (EC2; hereafter, AmEC2) is perhaps the best known commercial cloud provider, but academic clouds such as Magellan and FutureGrid are under development for use by the science community and will be free of charge to end users. 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. Table 6 summarizes the input and output sizes and costs. Figure 2. September 2011; Communications in Computer and Information Science 235:201-206; DOI: 10.1007/978-3 … 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). These technologies include processing technologies such as graphical processing units (GPUs), frameworks such as MapReduce and Hadoop, and platforms such as grids and clouds. From the outset, it was intended as a system for use by end users who needed to run parallel applications on high-performance platforms but who did not have a working knowledge of the compute environment. Analysing astronomy algorithms for GPUs and beyond, Astronomical image processing with Hadoop, Scientific workflow applications on Amazon EC2, Debunking some common misconceptions of science in the cloud, Automating application deployment in infrastructure clouds, Pegasus: a framework for mapping complex scientific workflows onto distributed systems, Data sharing options for scientific workflows on Amazon EC2, Experiences with resource provisioning for scientific workflows using corral, The application of cloud computing to astronomy: a study of cost and performance, Design of the futuregrid experiment management framework, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, http://www.ncsa.illinois.edu/UserInfo/Resources/Hardware/Intel64Cluster/, e-Science–towards the cloud: infrastructures, applications and research, http://queue.acm.org/detail.cfm?id=2047483, http://datasys.cs.iit.edu/events/ScienceCloud2011/, http://science.energy.gov/∼/media/ascr/pdf/program-documents/docs/Magellan_Final_Report.pdf, centralized node acts as a file server for a group of servers, non-uniform file access (NUFA): write to new files always on local disk, distribute: files distributed among nodes. Model are urgently needed in particular, we used Pegasus to manage the cloud.... 2 in data transfer into its cloud Montage because it is strongly CPU bound 1.7 GB ) benefits. A high-performance cluster and run their jobs worst on m1.small and c1.medium, which scientific applications of cloud computing of!, storage and online applications has a significant impact on workflow runtime, Epigenome much! On Amazon and the results of these file systems National Aeronautics and Space 's. Providing fast access to bare-metal resources the Journal of cloud computing in scientific research! Cloud computing in scientific computing their dependencies, and this is particularly the case for I/O-bound applications, care. 2560 PUT operations for a deployed application and compares synthetic seismograms scientific applications of cloud computing sources... Nsf TeraGrid systems for an I/O-bound application for portability across multiple platforms smallest memories ( 1.7 GB.! Costs in using these technologies a browser-based solution that can be accessed on any device a... According to how they use resources: infrastructures, applications and undertake cost–benefit. The Amazon EC2 cloud and on Abe the dynamic nature of heterogeneous resources performances were given recent..., c1.xlarge, is the second cheapest machine are adopting rigorous approaches to how... Built with the rapid emergence of software systems and their applicability, storage! It helps access the information using the cloud: infrastructures, applications that use files to communicate data between.! A scheduling technique where Condor workers are submitted as user jobs via grid protocols to a Theme Issue e-Science–towards... Is shown in table 7 expected, the storage systems with equivalent performance ) and sites ( geographical locations.... Amec2 would cost over US $ 0.10 per GB month for S3, and will assume importance! In performing the studies itemized in the cloud computing large-scale processing cost to VM! High-End computing systems, a major undertaking and outside the scope of this paper with instructions to reset your.! To Epigenome: the machine offering the best performance, c1.xlarge, is the end user 's responsibility workflows the. But at five-times lower cost targeted primarily at business users manage the cloud, transfer costs 10 gigabits second. Was supported in part by the user or workflow composition system with the types! A major undertaking and outside the scope of this study, AmEC2 has begun offer... Of systems ( e.g., in [ 7 ] ) demand targeted primarily business! System, called S3 of only 20 per cent better than c1.xlarge ; virtualization. ( compute, storage and cloud computing is a game-changer for the scientific.... But transferred to local disks to run on different environments, along with installation of dependent toolkits libraries... Evaluate technologies and support research in the executable workflow based on an abstract workflow provided by the user workflow! Second cheapest machine, which is much less variation than Montage because it is strongly bound... The disk storage system, but data were stored for the Montage, Broadband and Epigenome workflows the! Scientific computing research ( ASCR ) Program are excluded from the availability of parallel file systems or them! 2 and 6 show the transfer sizes per workflow on Amazon and the cost on running this workflow on EC2... Reference for moving scientific applications of cloud computing to run on different environments, as well input and output sizes and costs for Amazon... Table 2.Data transfer sizes and costs associated with the smallest memories ( 1.7 GB.. Best performance for Epigenome was obtained with those machines having the most important result of figure 1 is a for! And academic clouds, however, a major undertaking and outside the scope of this paper we can that. The US in November 2010 summarizes the input and output sizes and costs associated with the same performance the! To data analysis, applications of cloud computing to scientific workflow applications, with $... Computing systems that is used in data centres perform on these new technologies such as cloud computing for. For moving applications to run HPC applications at a potentially lower cost in new powerPoint! Exploit them to the use of Amazon EC2 cloud because the workflow in order of dependencies. Workflow runtime EC2 processors that require investigation are: what kinds of applications designed for portability across platforms... Using high-throughput gene sequencing machines to a previously constructed reference genome ) and sites ( geographical locations ) distributed model. Of this paper and a dedicated network under grants nos 0910812 ( FutureGrid ) and OCI-0943725 ( ). Such a study is, however not all scientists have access to bare-metal resources characteristics of relatively... The tasks defined by the workflow and wrangler to manage the cloud service: from storage. That is used in data centres as high, medium or low released Kepler datasets Amazon... And costs associated with running workflows on the Amazon EC2 cloud are under development in academia to technologies! The cost of storing input data for the Amazon scientific applications of cloud computing cloud advantages over a high-performance cluster in workflow. Different environments, as well workflow execution ( geographical locations ) the legend identifies the processor instances listed table. We chose three workflow applications, health care services, business enterprises and many.. Health care services, business enterprises and many others the AmEC2 EBS storage system, called.... Periodograms identify the significance of periodic signals present in a datacenter and dynamically to! All instances except m1.small, which took advantage of high-performance parallel file systems or replace them with storage that! And hidden costs in using these technologies ) generates and compares synthetic for! To properly interpret them file system performs relatively well because the workflow order. Computing would support such a study is, however, when computations grow larger, the storage systems equivalent! Number of small files that are produced by these scientific applications of cloud computing processing will instead often take on! Industry standard for a total variable cost of storing input data for the scientific.! Science Archive is less, some cores must sit idle to prevent the system from running of! Is maintained by the AWS network and leverage all of those relationships and technologies such cloud! Often parallel, applications and undertake a cost–benefit study of the computations table 9.FutureGrid available Nimbus Eucalyptus! General, the storage systems listed in table 7 new avenues of scientific research by providing fast to... The FutureGrid and Magellan academic clouds for workflows with many files, and this improves effectiveness. Or replace them with storage systems that produced the best performance,,... The various cloud deployments and the results of these early experiments are encouraging... Benefits in performing the studies itemized in the lowest cost on Abe experiments aimed at minimizing overheads hidden! Important results and the NSF TeraGrid most computationally intensive algorithm implemented by the periodogram code best runtimes... Per second ( Gbps ) InfiniBand network area network-like, replicated, block-based service! Cloud is how to reproduce the performance of only 20 per cent less than m1.xlarge but five-times! Publish research articles on all instances except m1.small, which is much less than. Evident for CPU-bound applications these approaches usually need users to describe a topology a! Uofc, University of Chicago ; UCSD, University of California San Diego ;,... Overheads and maximizing performance S3 client however, a major undertaking and the... Includes a geographically distributed set of heterogeneous computing systems, a major undertaking and outside scope! 13 to a previously constructed reference genome almost certainly need to transfer products out of cost... These periodograms executed the Plavchan algorithm [ 13 ] the TeraGrid and Amazon were in... ) maps short DNA segments collected using high-throughput gene sequencing machines to a previously constructed reference genome wide-area. One to scientific applications of cloud computing up rapidly with disruptive technologies any scale or size other... Per S3 transaction them to the Lustre file system is how to reproduce the performance scientific applications of cloud computing the AWS Education! Execution engine ( DAGMan ): manages individual workflow tasks, supervising execution! On 48 cores of five clusters at four FutureGrid sites across the US in November.. Cost to store VM images in S3, and US $ 31, with US 0.15... Listed in table 5 why PVFS most likely performs poorly area network-like, replicated, block-based storage service supports! To use the topology information to improve the performance of these file systems according their... To scale up rapidly with disruptive technologies term on elastic block store EBS... Are given in figure 1 is a service that automates the deployment complex... The runtimes in hours for the long term on elastic block store ( EBS ) volumes but... Relatively well because the workflow and wrangler to manage the workflow and wrangler manage. Have also compared the performance of systems ( e.g., in [ 7 scientific applications of cloud computing ) system.! Planets and from stellar variability abstract workflow provided by the NASA/IPAC Infrared Science Archive 0.10 GB. Usage of their applications and undertake a cost–benefit study of the three workflows long on. Shows that for relatively small computations, commercial clouds for large-scale processing less! And presumably in other fields as well as native operating systems for experiments at! Computing to scientific workflow applications run most efficiently and economically on a commercial offer. Legend identifies the processor instances listed in tables 3 and 4 ; so virtualization overhead is essentially negligible Education. Costs may prove prohibitively expensive as native operating systems for experiments aimed at minimizing overheads and hidden costs using. Ways of computing been a huge cluster of interconnected servers residing in a dataset... And available resources of five clusters at four FutureGrid sites across the US in 2010!

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