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


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



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).


  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).
  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).
  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).
  5. 5.
    Singh, S., Chana, I.: A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges. Journal of Grid Computing (2016).
  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).
  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).
  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).
  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).
  14. 14.
    OGC: Title of subordinate document. In: Contract Management Guidelines. Available via DIALOG (2002).
  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).
  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).
  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).
  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).
  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).
  23. 23.
    Yangui, S., Marshall, I., Laisne, J., Tata, S.: CompatibleOne: The open source cloud broker. Journal of Grid Computing (2014).
  24. 24.
    Ngan, L., Kanagasabai, R.: OWL-S based semantic cloud service broker. In: IEEE International Conference on Web Services (2012).
  25. 25.
    Saaty, R.: The analytic hierarchy process – What it is and how it is used. Mathematical Modeling (1987).
  26. 26.
  27. 27.
    Google, Google Compute Engine.
  28. 28.
    Google, Google App Engine.
  29. 29.
    Microsoft, Microsoft Office 365.
  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,
  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).
  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).
  38. 38.
    Microsoft, Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio.
  39. 39.
    Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Computer Science (2013).
  40. 40.
    Ayadi, I., Simoni, N., Aubonnet, T.: SLA approach for “Cloud as a Service”. In: International Conference on Cloud Computing (2013).
  41. 41.
    Alhamad, M., Dillon, T., Chang, E.: Conceptual SLA framework for cloud computing. In: International Conference on Digital Ecosystems and Technologies (2010).
  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).
  43. 43.
    Hamadache, K., Rizou, S.: Holistic SLA ontology for cloud service evaluation. In: International Conference on Advanced Cloud and Big Data (2013).
  44. 44.
    Google, Google Compute Engine 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

Personalised recommendations