zbMATH — the first resource for mathematics

Cloud computing for small research groups in computational science and engineering: current status and outlook. (English) Zbl 1234.68036
Summary: Cloud computing could offer good business models for small computational science and engineering (CSE) research groups because these groups often do not have enough human resources and knowledge to manage the complexity of computational and data infrastructure for their research, while cloud computing aims to eliminate that complexity from the user. In this paper, we analyze current status of supporting tools for small CSE groups to utilize cloud computing. Although cloud computing is perceived as an interesting model, we have identified several issues that prevent a wide adoption of cloud computing from small CSE research groups. We also present recommendations for addressing these issues in order to attract small CSE groups to utilize cloud computing.
68M14 Distributed systems
68U35 Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.)
Full Text: DOI
[1] 3tera AppLogic (2009) http://www.3tera.com/AppLogic/ . Accessed 8 July 2009
[2] Ahern S, Alam S, Fahey M, Hartman-Baker R, Barrett R, Kendall R, Kothe D, Messer O, Mills R, Sankaran R, Tharrington A, White JB III (2007) Scientific application requirements for leadership computing at the exascale. Technical report, Oak Ridge National Laboratory. http://www.nccs.gov/wp-content/media/nccs_reports/Exascale_Reqms.pdf
[3] Appistry CloudIQ Platform (2009) http://www.appistry.com/products . Accessed 8 July 2009
[4] Appnexus (2009) http://www.appnexus.com/ . Accessed 2 July 2009
[5] Apprenda SaaSGrid (2009) http://www.apprenda.com/ . Accessed 8 July 2009
[6] Aptana Cloud (2009) http://www.aptana.com/cloud . Accessed 8 July 2009
[7] Armbrust M, Fox A, Grifth R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: A Berkeley view of cloud computing. Technical report. University of California at Berkeley, URL http://www.berkeleyclouds.blogspot.com/2009/02/above-clouds-released.html
[8] Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4): 50–58 · Zbl 05748173 · doi:10.1145/1721654.1721672
[9] Avetisyan AI, Campbell R, Gupta I, Heath MT, Ko SY, Ganger GR, Kozuch MA, O’Hallaron D, Kunze M, Kwan TT, Lai K, Lyons M, Milojicic DS, Lee HY, Soh YC, Ming NK, Luke JY, Namgoong H (2010) Open cirrus: a global cloud computing testbed. Computer 43:35–43. http://www.ieeecomputersociety.org/10.1109/MC.2010.111
[10] Boomi AtomSphere (2009) http://www.boomi.com/ . Accessed 9 July 2009
[11] Buyya R, Yeo CS, Venugopal S (2008) Market-oriented cloud computing: vision, hype, and reality for delivering it services as computing utilities. In: 10th IEEE international conference on high performance computing and communications, vol 0, pp 5–13. http://www.ieeecomputersociety.org/10.1109/HPCC.2008.172
[12] Chine K (2010) Open science in the cloud: towards a universal platform for scientific and statistical computing. In: Furht B, Escalante A (eds) Handbook of cloud computing, Springer, USA, pp 453–474. http://dx.doi.org/10.1007/978-1-4419-6524-0_19 , ISBN 978-1-4419-6524-0
[13] Cohesive FT (2009) http://www.cohesiveft.com/ . Accessed 2 July 2009
[14] Connectors AC (2009) http://www.appirio.com/products/cloudconnectors.php . Accessed 16 June 2009
[15] de Assuncao MD, di Costanzo A, Buyya R (2009) Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters. In: HPDC ’09: Proceedings of the 18th ACM international symposium on high performance distributed computing, ACM, New York, NY, USA, pp 141–150. http://www.acm.org/10.1145/1551609.1551635
[16] Deelman E, Singh G, Livny M, Berriman B, Good J (2008) The cost of doing science on the cloud: the montage example. In: SC’08: Proceedings of the 2008 ACM/IEEE conference on supercomputing, IEEE Press, Piscataway, NJ, USA, pp 1–12. http://www.acm.org/10.1145/1413370.1413421
[17] Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-science: an overview of workflow system features and capabilities. Future Gener Comput Syst 25(5):528–540. http://dx.doi.org/10.1016/j.future.2008.06.012
[18] Dynamite (2009) http://www.s12-ap550.bioch.ox.ac.uk:8078/dynamite_html/collect_data_v1.5.html . accessed: 16 June 2009
[19] EC2 AECCA (2009) http://www.aws.amazon.com/ec2/ . Accessed 2 July 2009
[20] ElasticHosts (2009) http://www.elastichosts.com/ . Accessed 2 July 2009
[21] Elastra (2009) http://www.elastra.com/ . Last access: 16 June 2009
[22] Erdogmus H (2009) Cloud computing: Does nirvana hide behind the nebula? IEEE Software 26(2):4–6. http://www.ieeecomputersociety.org/10.1109/MS.2009.31
[23] Evangelinos C, Hill C (2008) Cloud computing for parallel scientific HPC applications: feasibility of running coupled atmosphere-ocean climate models on Amazon’s ec2. In: The first workshop on cloud computing and its applications (CCA’08). http://www.cca08.org/papers/Paper34-Chris-Hill.pdf
[24] Factory TP (2009) http://www.theprocessfactory.com/ . accessed: 16 June 2009
[25] Foster IT, Zhao Y, Raicu I, Lu S (2009) Cloud computing and grid computing 360-degree compared. CoRR abs/0901.0131
[26] Garfinkel SL (2007) An evaluation of amazon’s grid computing services: Ec2, s3 and sqs. Technical report. TR-08-07, Harvard University. http://www.ftp.deas.harvard.edu/techreports/tr-08-07.pdf
[27] Geelan J (2009) Cloud computing: 100 players in the cloud computing ecosystem. http://www.cloudcomputing.sys-con.com/node/770174
[28] GigaSpaces (2009) http://www.gigaspaces.com/ . Accessed 8 July 2009
[29] Google App Engine (2009) http://www.code.google.com/appengine/ . Accessed 9 July 2009
[30] Hoffa C, Mehta G, Freeman T, Deelman E, Keahey K, Berriman B, Good J (2008) On the use of cloud computing for scientific workflows. In: IEEE international conference on eScience, vol 0, pp 640–645. http://www.ieeecomputersociety.org/10.1109/eScience.2008.167
[31] Joyent (2009) http://www.joyent.com/ . Accessed 2 July 2009
[32] Keahey K, Freeman T (2008) Science clouds: early experiences in cloud computing for scientific applications. Cloud computing and applications
[33] Keahey K, Tsugawa M, Matsunaga A, Fortes J (2009) Sky computing. IEEE Internet Comput 13:43–51. http://www.ieeecomputersociety.org/10.1109/MIC.2009.94
[34] Kondo D, Javadi B, Malecot P, Cappello F, Anderson DP (2009) Cost-benefit analysis of cloud computing versus desktop grids. In: IPDPS ’09: Proceedings of the 2009 IEEE international symposium on parallel & distributed processing, IEEE Computer Society, Washington, DC, USA, pp 1–12. http://dx.doi.org/10.1109/IPDPS.2009.5160911
[35] Kothe D, Kendall R (2007) Computational science requirements for leadership computing. Technical report, Oak Ridge National Laboratory. http://www.nccs.gov/wp-content/media/nccs_reports/ORNL_TM-2007_44.pdf
[36] Long Jump (2009) http://www.longjump.com/ . Accessed 9 July 2009
[37] Mell P, Grance T (2009) The Nist definition of cloud computing. http://www.csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc , v15
[38] Microsoft Azure Services Platform (2009) http://www.microsoft.com/azure/default.mspx . Accessed 8 July 2009
[39] Mietzner R, Unger T, Leymann F (2009) Cafe: a generic configurable customizable composite cloud application framework. In: Meersman R, Dillon TS, Herrero P (eds) OTM conferences (1), Springer, Lecture notes in computer science, vol 5870, pp 357–364
[40] Moreno-Vozmediano R, Montero RS, Llorente IM (2009) Elastic management of cluster-based services in the cloud. In: ACDC ’09: Proceedings of the 1st workshop on automated control for datacenters and clouds, ACM, New York, NY, USA, pp 19–24, http://www.acm.org/10.1145/1555271.1555277
[41] Nilsen JK (2007) Python in scientific computing: applications to Bose–Einstein condensates. Comput Phys Commun 177(1–2): 45 · doi:10.1016/j.cpc.2007.02.093
[42] Nurmi D, Wolski R, Grzegorczyk C, Obertelli G, Soman S, Youseff L, Zagorodnov D (2009) The eucalyptus open-source cloud-computing system. In: Cappello F, Wang CL, Buyya R (eds) CCGRID, IEEE Computer Society, pp 124–131
[43] Palankar MR, Iamnitchi A, Ripeanu M, Garfinkel S (2008) Amazon s3 for science grids: a viable solution? In: DADC ’08: Proceedings of the 2008 international workshop on Data-aware distributed computing, ACM, New York, NY, USA, pp 55–64. http://www.acm.org/10.1145/1383519.1383526
[44] Parabon Frontier (2009) http://www.parabon.com/ . Accessed 8 July 2009
[45] Paraview (2009) http://www.paraview.org/ . Accessed 16 June 2009
[46] Pautasso C, Heinis T, Alonso G (2006) Jopera: autonomic service orchestration. IEEE Data Eng Bull 29(3): 32–39
[47] Sameh A, Cybenko G, Kalos M, Neves K, Rice J, Sorensen D, Sullivan F (1996) Computational science and engineering. ACM Comput Surv 28(4):810–817. http://doi.acm.org/10.1145/242223.246865
[48] Science VC, 2009 EW (2009) http://www.infosys.tuwien.ac.at/autocompwiki/index.php/CSE09 . Accessed 16 June 2009
[49] Simpson AD, Bull M, Hill J (2008) Identification and categorisation of applications and initial benchmarks suite. http://www.prace-project.eu/documents/Identification_and_Categorisatio_of_Applications_and_Initial_Benchmark_Suite_final.pdf
[50] Six Benefits of Cloud Computing CCND (2008) http://www.web2.sys-con.com/node/640237
[51] Skytap (2009) http://www.skytap.com . Accessed 2 July 2009
[52] SOASTA (2009) http://www.soasta.com/ . Accessed 16 June 2009
[53] Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22. http://dx.doi.org/10.1109/MIC.2009.119
[54] Sterling T, Stark D (2009) A high-performance computing forecast: partly cloudy. Comput Sci Eng 11(4):42–49. http://www.ieeecomputersociety.org/10.1109/MCSE.2009.111
[55] Sullivan F (2009) Guest editor’s introduction: Cloud computing for the sciences. Comput Sci Eng 11(4):10–11. http://www.ieeecomputersociety.org/10.1109/MCSE.2009.121
[56] Taylor IJ, Deelman E, Gannon DB, Shields M (2006) Workflows for e-Science: scientific workflows for grids. Springer New York, Inc., Secaucus, NJ, USA
[57] Truong HL, Dustdar S (2010) Composable cost estimation and monitoring for computational applications in cloud computing environments. In: The international conference on computational science 2010 (ICCS 2010). Tools for program development and analysis in computational science, Elsevier, Amsterdam, The Netherlands
[58] VAMP/VASP (2009) http://www.cms.mpi.univie.ac.at/vasp . Accessed 16 June 2009
[59] Vaquero LM, Rodero-Merino L, Caceres J, Lindner M (2009) A break in the clouds: towards a cloud definition. SIGCOMM Comput Commun Rev 39(1):50–55. http://www.acm.org/10.1145/1496091.1496100
[60] Walker E, Brisken W, Romney J (2010) To lease or not to lease from storage clouds. Computer 43: 44–50. http://doi.ieeecomputersociety.org/10.1109/MC.2010.115
[61] Wang L, Tao J, Kunze M, Castellanos AC, Kramer D, Karl W (2008) Scientific cloud computing: early definition and experience. In: HPCC, IEEE, pp 825–830
[62] Watson GR, DeBardeleben NA (2006) Developing scientific applications using eclipse. Comput Sci Eng 8(4):50–61. http://www.dx.org/10.1109/MCSE.2006.64
[63] Wien2k (2009) http://www.wien2k.at . Accessed 16 June 2009
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.