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Journal of Grid Computing

, Volume 17, Issue 1, pp 137–168 | Cite as

C-RCE: an Approach for Constructing and Managing a Cloud Service Broker

  • Joonseok Park
  • Ungsoo Kim
  • Donggyu Yun
  • Keunhyuk YeomEmail author
Article
  • 43 Downloads

Abstract

Recent years have seen a paradigm shift from PC-centric computing to cloud computing. The advent of cloud computing has led to the emergence of various cloud services and providers. Cloud service brokers (CSBs) were introduced to serve as intermediaries between cloud service providers and cloud users who wish to select an appropriate cloud service. A CSB requires intermediation technologies with service recommendation, contract management, and cloud service usage assistance (such as evaluation) capabilities. These intermediation technologies enable CSBs to increase the quality of cloud service usage. However, currently commercially available CSBs fail to satisfy user requirements. In addition, many open research problems remain in the technologies and approaches underpinning CSB intermediation technologies. This paper proposes Cloud Service—Recommendation, Contract, and Evaluation (C-RCE), which supports CSB processes, including the management and operation of each proposed process. We implement a prototype of the proposed C-RCE process in a CSB to evaluate its performance and confirm that it is superior to existing CSBs. The proposed C-RCE process may be used as a guideline and reference model for constructing, operating, and managing actual CSBs.

Keywords

Cloud service brokerage Cloud service recommendation Cloud service contract Cloud service evaluation Cloud service governance 

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Notes

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03935865).

References

  1. 1.
    Liu, F., Tong, J., Mao, J., Bohn, R., Messina, J., Badger, L., Leaf, D.: NIST Cloud Computing Reference Architecture. NIST Special Publication (2011)Google Scholar
  2. 2.
    Garcia, A., Blanquer, I.: Cloud Services Representation using SLA Composition. Journal of Grid Computing (2015).  https://doi.org/10.1007/s10723-014-9295-6
  3. 3.
    Cuomo, A., Modica, G., Distefano, S., Puliafito, A., Rak, M., Tomarchio, O., Venticinque, S., Villano, U.: An SLA-based Broker for Cloud Infrastructures. Journal of Grid Computing (2013).  https://doi.org/10.1007/s10723-012-9241-4
  4. 4.
    Rimal, B., Jukan, A., Katsaros, D., Goeleven, Y.: Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud Approach. Journal of Grid Computing (2011).  https://doi.org/10.1007/s10723-010-9171-y
  5. 5.
    Singh, S., Chana, I.: A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges. Journal of Grid Computing (2016).  https://doi.org/10.1007/s10723-015-9359-2
  6. 6.
    Park, J., An, Y., Kang, T., Yeom, K.: Virtual cloud bank: consumer-centric service recommendation process and architectural perspective for cloud service brokers. Computing 98, 1153–1184 (2016)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Park, J., An, Y., Yeom, K.: Virtual cloud bank: an architectural approach for intermediating cloud services. In: IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).  https://doi.org/10.1109/SNPD.2015.7176235(2015)
  8. 8.
    Park, J., An, Y., Yeom, K.: Virtual Cloud Bank: Cloud Service Broker for Intermediating Services Based on Semantic Analysis Models. In: IEEE Intl Conf on Ubiquitous Intelligence and Computing and IEEE Intl Conf on Autonomic and Trusted Computing and IEEE Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) (2015).  https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.191
  9. 9.
    Neukrug, E., Fawcett, C.: Essentials of testing and assessment: A practical guide for counselors, social workers, and psychologists. Cengage Learning (2006)Google Scholar
  10. 10.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J. 5, 1093–1113 (2015)CrossRefGoogle Scholar
  11. 11.
    Baranwal, G., Vidyarthi, D.: A cloud service selection model using improved ranked voting method. Concurrency and Computation: Practice and Experience 28, 3540–3567 (2014)CrossRefGoogle Scholar
  12. 12.
    Garg, S., Versteeg, S., Buyya, R.: SMIcloud: A framework for comparing and ranking cloud services. In: IEEE International Conference on Utility and Cloud Computing (2011).  https://doi.org/10.1109/UCC.2011.36
  13. 13.
    Wang, S., Liu, Z., Sun, Q., Zou, H., Yang, F.: Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. Journal of Intelligent Manufacturing (2014).  https://doi.org/10.1007/s1084501206616
  14. 14.
    OGC: Title of subordinate document. In: Contract Management Guidelines. Available via DIALOG (2002). http://prp.gov.wales/docs/prp/generalgoodsservices/130617ogccontractmgtguidance.pdf
  15. 15.
    Zhang, Z., Liao, L., Liu, H., Li, G.: Policy-based adaptive service level agreement management for cloud services. In: IEEE International Conference on Software Engineering and Service Science (2014).  https://doi.org/10.1109/ICSESS.2014.6933614
  16. 16.
    Zhu, F., Li, H., Lu, J.: A service level agreement framework of cloud computing based on the cloud bank model. In: IEEE International Conference on Computer Science and Automation Engineering (2012).  https://doi.org/10.1109/CSAE.2012.6272592
  17. 17.
    Venticinque, S., Aversa, R., Martino, B., Rak, M., Petcu, D.: A cloud agency for SLA negotiation and management. Euro-Par 2010 Parallel Processing Workshops Lecture Notes in Computer Science (2011)Google Scholar
  18. 18.
    Kritikos, K., Pernici, B., Plebani, P., Cappiello, C., Comuzzi, M., Benbernou, S., Brandic, I., Kertesz, A., Parkin, M., Caro, M.: A survey on service quality description. ACM Comput Surv (2013).  https://doi.org/10.1145/2522968.2522969
  19. 19.
    Jrad, F., Tao, J., Streit, A.: SLA based service brokering in intercloud environments. CLOSER (2012)Google Scholar
  20. 20.
    Badidi, E.: A cloud service broker for SLA-based SaaS provisioning. In: International Conference on Information Society, pp. 61–66 (2013)Google Scholar
  21. 21.
    Khanna, P., Jain, S., Babu, B.: BroCUR: distributed cloud broker in a cloud federation: brokerage peculiarities in a hybrid cloud. International Conference on Computing, Communication & Automation(ICCCA) (2015).  https://doi.org/10.1109/CCAA.2015.7148472
  22. 22.
    Li, X., Ma, H., Zhou, F., Yao, W.: T-broker: A trust-aware service brokering scheme for multiple cloud collaborative services. IEEE Transactions on Information Forensics and Security (2015).  https://doi.org/10.1109/TIFS.2015.2413386
  23. 23.
    Yangui, S., Marshall, I., Laisne, J., Tata, S.: CompatibleOne: The open source cloud broker. Journal of Grid Computing (2014).  https://doi.org/10.1007/s10723-013-9285-0
  24. 24.
    Ngan, L., Kanagasabai, R.: OWL-S based semantic cloud service broker. In: IEEE International Conference on Web Services (2012).  https://doi.org/10.1109/ICWS.2012.103
  25. 25.
    Saaty, R.: The analytic hierarchy process – What it is and how it is used. Mathematical Modeling (1987).  https://doi.org/10.1016/0270-0255(87)90473-8
  26. 26.
  27. 27.
    Google, Google Compute Engine. https://cloud.google.com/compute
  28. 28.
    Google, Google App Engine. https://cloud.google.com/appengine
  29. 29.
    Microsoft, Microsoft Office 365. https://www.microsoft.com/en-US/store/b/office365
  30. 30.
    An, Y., Park, J., Yeom, K.: Quality metrics of cloud service based on cross-cutting and SLA specification mechanism. Journal of KIISE 42, 1361–1371 (2015). In KoreanCrossRefGoogle Scholar
  31. 31.
    McConnell, S.: Real Quality For Real Engineers, http://www.stevemcconnell.com/ieeesoftware/eic22.htm
  32. 32.
    Reiss, G.: Project Management Demystified: Today’s Tools and Techniques. Routledge, Abingdon (2013)Google Scholar
  33. 33.
  34. 34.
    Villegas, D., Sadjadi, S.: Mapping non-functional requirements to cloud applications. In: International Conference on Software Engineering and Knowledge Engineering, pp. 527–532 (2011)Google Scholar
  35. 35.
    Hosono, S., Hara, T., Shimomura, Y., Arai, T.: Prioritizing Service Functions with Non-Functional Requirements. CIRP Industrial Product-Service Syst Conf 77, 133–140 (2010)Google Scholar
  36. 36.
    Siegel, J., Perdue, J.: Cloud services measures for global use: The Service Measurement Index (SMI). In: IEEE SRII Global Conference (2012).  https://doi.org/10.1109/SRII.2012.51
  37. 37.
    Alkalbani, A., Ghamry, A., Hussain, F., Hussain, O.: Sentiment analysis and classification for software as a service reviews. In: IEEE International Conference on Advanced Information Networking and Applications (2016).  https://doi.org/10.1109/AINA.2016.148
  38. 38.
    Microsoft, Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio. https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
  39. 39.
    Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Computer Science (2013).  https://doi.org/10.1016/j.procs.2013.05.005
  40. 40.
    Ayadi, I., Simoni, N., Aubonnet, T.: SLA approach for “Cloud as a Service”. In: International Conference on Cloud Computing (2013).  https://doi.org/10.1109/CLOUD.2013.127
  41. 41.
    Alhamad, M., Dillon, T., Chang, E.: Conceptual SLA framework for cloud computing. In: International Conference on Digital Ecosystems and Technologies (2010).  https://doi.org/10.1109/DEST.2010.5610586
  42. 42.
    Stamou, K., Kantere, V., Morin, J., Geogiou, M.: A SLA graph model for data services. In: International Workshop on Cloud data management (2013).  https://doi.org/10.1145/2516588.2516592
  43. 43.
    Hamadache, K., Rizou, S.: Holistic SLA ontology for cloud service evaluation. In: International Conference on Advanced Cloud and Big Data (2013).  https://doi.org/10.1109/CBD.2013.18
  44. 44.
    Google, Google Compute Engine SLA. https://cloud.google.com/compute/sla
  45. 45.
  46. 46.

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Joonseok Park
    • 1
  • Ungsoo Kim
    • 2
  • Donggyu Yun
    • 2
  • Keunhyuk Yeom
    • 2
    Email author
  1. 1.Research Institute of Logistics Innovation and NetworkingPusan National UniversityBusanSouth Korea
  2. 2.Department of Electrical and Computer EngineeringPusan National UniversityBusanSouth Korea

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