Advertisement

Aspects of Data-Intensive Cloud Computing

  • Sebastian Frischbier
  • Ilia Petrov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6462)

Abstract

The concept of Cloud Computing is by now at the peak of public attention and adoption. Driven by several economic and technological enablers, Cloud Computing is going to change the way we have to design, maintain and optimise large-scale data-intensive software systems in the future. Moving large-scale, data-intensive systems into the Cloud may not always be possible, but would solve many of today’s typical problems. In this paper we focus on the opportunities and restrictions of current Cloud solutions regarding the data model of such software systems. We identify the technological issues coming along with this new paradigm and discuss the requirements to be met by Cloud solutions in order to provide a meaningful alternative to on-premise configurations.

Keywords

Cloud Computing Grid Computing Cloud Service Application Programming Interface Cloud Provider 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    YouTube: Youtube - broadcast yourself (2010), http://www.youtube.com/
  2. 2.
    Yahoo!: Flickr (2010), http://www.flickr.com
  3. 3.
    Ebay: ebay - new & used electronics, cars, apparel, collectibles, sporting goods & more at low prices (2010), http://www.ebay.com
  4. 4.
    Google: Google picasa (2010), http://www.google.com/picasa
  5. 5.
    Microsoft: Microsoft hotmail (2010), http://www.hotmail.com
  6. 6.
    Amazon.com: Online shopping for electronics, apparel, computers, books, dvds & more (2010), http://www.amazon.com
  7. 7.
    Google: Google documents and spreadsheets (2010), http://www.google.com/docs/
  8. 8.
    Facebook: Facebook (2010), http://www.facebook.com
  9. 9.
    Google: Google maps (2010), http://maps.google.com/
  10. 10.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: A view of cloud computing. Communications of the ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  11. 11.
    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, 2008. GCE 2008, pp. 1–10 (2008)Google Scholar
  12. 12.
    Mell, P., Grance, T.: The NIST Definition of Cloud Computing. National Institute of Standards and Technology, Information Technology Laboratory (July 2009)Google Scholar
  13. 13.
    Bittman, T.: A better cloud computing analogy (September 2009), http://blogs.gartner.com/thomas_bittman/2009/98/22/a-better-cloud-computing-analogy/
  14. 14.
    Giordanelli, R., Mastroianni, C.: The cloud computing paradigm: Characteristics, opportunities and research issues. Technical Report RT-ICAR-CS-10-01, Consiglio Nazionale delle Ricerche Istituto di Calcolo e Reti ad Alte Pestazioni (April 2010)Google Scholar
  15. 15.
    Foster, I.: What is the grid? A three point checklist. GRID Today 1(6), 22–25 (2002)Google Scholar
  16. 16.
    Mäkilä, T., Järvi, A., Rönkkö, M., Nissilä, J.: How to define software-as-a-service - an empirical study of finnish saas providers. In: Tyrväinen, P. (ed.) ICSOB 2010. Lecture Notes in Business Information Processing, vol. 51, pp. 115–124. Springer, Heidelberg (2010)Google Scholar
  17. 17.
    Ross, J.W., Westerman, G.: Preparing for utility computing: The role of it architecture and relationship management. IBM Systems Journal 43(1), 5–19 (2004)CrossRefGoogle Scholar
  18. 18.
    Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2009)CrossRefGoogle Scholar
  19. 19.
    Church, K., Greenberg, A., Hamilton, J.: On delivering embarrassingly distributed cloud services. Hotnets VII (2008)Google Scholar
  20. 20.
    Patel, C.D., Shah, A.J.: Cost model for planning, development and operation of a data center. Technical report, HP Laboratories Palo Alto (June 2005)Google Scholar
  21. 21.
    Banks, D., Erickson, J., Rhodes, M.: Multi-tenancy in cloud-based collaboration services. Technical Report HPL-2009-17, HP Laboratories (February 2009)Google Scholar
  22. 22.
    Grossman, R.L.: The case for cloud computing. IT Professional 11(2), 23–27 (2009)CrossRefGoogle Scholar
  23. 23.
    Han, L.: Market Acceptance of Cloud Computing - An Analysis of Market Structure, Price Models and Service Requirements. Bayreuth Reports on Information Systems Management, p. 42 Universität Bayreuth (April 2009)Google Scholar
  24. 24.
    Varia, J.: Architecting for the cloud: Best practices (January 2010), http://jineshvaria.s3.amazonaws.com/public/cloudbestpractices-jvaria.pdf
  25. 25.
    Amazon.com: Amazon web services (2010), http://aws.amazon.com
  26. 26.
    Reese, G.: Cloud Application Architectures: Transactional Systems for EC2 and Beyond, 1st edn. O’Reilly, Sebastopol (2009)Google Scholar
  27. 27.
  28. 28.
    Google: Google spreadsheets api (2010), http://code.google.com/intl/en/apis/spreadsheets/
  29. 29.
    Google: Google document list api (2010), http://code.google.com/intl/en/apis/documents/
  30. 30.
    Google: Gmail apis and tools (2010), http://code.google.com/intl/en/apis/gmail/
  31. 31.
    Microsoft: Windows Azure (2010), http://www.microsoft.com/windowsazure/windowsazure/
  32. 32.
    Microsoft: SQL Azure - database as a service (2010), http://www.microsoft.com/windowsazure/sqlazure/
  33. 33.
    Woollen, R.: The internal design of salesforce.com’s multi-tenant architecture. In: Proceedings of the 1st ACM symposium on Cloud computing, SoCC 2010, pp. 161–161. ACM, New York (2010)Google Scholar
  34. 34.
    Schroeder, B., Pinheiro, E., Weber, W.D.: Dram errors in the wild: a large-scale field study. In: Proceedings of the eleventh International Joint Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2009, pp. 193–204. ACM Press, New York (2009)Google Scholar
  35. 35.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D.A., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: A berkeley view of cloud computing. Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley (February 2009)Google Scholar
  36. 36.
    Appel, S.: Analysis and Modeling of Application Behavior in Virtualized Environments. Master’s thesis, Technische Universität Darmstadt (2009)Google Scholar
  37. 37.
    Stonebraker, M.: Sql databases v. nosql databases. ACM Commun. 53(4), 10–11 (2010)CrossRefGoogle Scholar
  38. 38.
    Sobel, J.: Building facebook: performance at massive scale. In: Proceedings of the 1st ACM symposium on Cloud computing, SoCC 2010, pp. 87–87. ACM, New York (2010)Google Scholar
  39. 39.
    Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 1099–1110. ACM Press, New York (2008)CrossRefGoogle Scholar
  40. 40.
    Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: Proceedings of the Sixth Symposium on Operating Systems Design and Implementation, OSDI 2004, pp. 137–150 (December 2004)Google Scholar
  41. 41.
    Yahoo!: The hadoop project (2010), http://hadoop.apache.org/core/
  42. 42.
    Brantner, M., Florescu, D., Graf, D., Kossmann, D., Kraska, T.: Building a database on s3. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 251–264. ACM Press, New York (2008)CrossRefGoogle Scholar
  43. 43.
    Vogels, B.Y.W.: Eventually consistent. Communications of the ACM 52(1), 40–44 (2009)CrossRefGoogle Scholar
  44. 44.
    Stonebraker, M., Abadi, D., DeWitt, D.J., Madden, S., Paulson, E., Pavlo, A., Rasin, A.: Mapreduce and parallel dbmss: friends or foes? ACM Commun. 53(1), 64–71 (2010)CrossRefGoogle Scholar
  45. 45.
    Yang, H., Tate, M.: Where are we at with cloud computing? In: Proceedings of the 20th Australasian Conference on Information Systems, ACIS 2009, pp. 807–819 (2009)Google Scholar
  46. 46.
    Gilbert, S., Lynch, N.: Brewer’s conjecture and the feasibility of consistent available partition-tolerant web services. ACM SIGACT News 33(2), 51–59 (2002)CrossRefGoogle Scholar
  47. 47.
    Finkelstein, S., Brendle, R., Jacobs, D.: Principles for inconsistency. In: Proceedings of the 4th Biennial Conf. on Innovative Data Systems Research (CIDR), Asilomar, CA, USA (2009)Google Scholar
  48. 48.
    Brown, A.B., Patterson, D.A.: Embracing failure: A case for recovery-oriented computing (roc). In: High Performance Transaction Processing Symposium, vol. 10, pp. 3–8 (2001)Google Scholar
  49. 49.
    Binnig, C., Kossmann, D., Kraska, T., Loesing, S.: How is the weather tomorrow? towards a benchmark for the cloud. In: Proceedings of the Second International Workshop on Testing Database Systems (DBTest 2009). ACM, New York (2009)Google Scholar
  50. 50.
    Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. In: Proceedings of the 7th symposium on Operating Systems Design and Implementation, OSDI 2006, pp. 205–218. USENIX Association, Berkeley (2006)Google Scholar
  51. 51.
    Team, H.D.: Hbase: Bigtable-like structured storage for hadoop hdfs (2007), http://wiki.apache.org/lucene-hadoop/Hbase
  52. 52.
    Hypertable.org: Hypertable (2010), http://hypertable.org/
  53. 53.
    Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. SIGOPS Oper. Syst. Rev. 44(2), 35–40 (2010)CrossRefGoogle Scholar
  54. 54.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation, OSDI 2004, pp. 10–10. USENIX Association, Berkeley (2004)Google Scholar
  55. 55.
    Project, M.: What is memcached? (2010), http://memcached.org/
  56. 56.
    Fenn, J., Raskino, M., Gammage, B.: Gartner’s hype cycle special report for 2009 (2009)Google Scholar
  57. 57.
    Dubey, A., Mohiuddin, J., Baijal, A.: Emerging Platform Wars in Enterprise Software. Technical report, McKinsey & Company (2008)Google Scholar
  58. 58.
    Dubey, A., Mohiuddin, J., Baijal, A.: Enterprise Software Customer Survey 2008. Customer survey, McKinsey & Company, SandHill Group (2008)Google Scholar
  59. 59.
    Gens, F.: Top 10 predictions: Idc predictions 2010: Recovery and transformation. Survey, IDC (December 2009)Google Scholar
  60. 60.
    Hagiu, A., Yoffie, D.B.: What’s your google strategy? Harvard Business Review, 74–81 (April 2009)Google Scholar
  61. 61.
    Thethi, J.P.: Realizing the value proposition of cloud computing. Technical report, Infosys (April 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sebastian Frischbier
    • 1
  • Ilia Petrov
    • 1
  1. 1.Databases and Distributed Systems GroupTechnische Universität DarmstadtGermany

Personalised recommendations