International Journal of Distributed and Cloud Computing

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Automation of Discovery and Aggregation of Cloud Services


Affiliations
1 Department of Computer Science and Engineering, KLE DR MSS College of Engineering and Technology, Belaeavi, Karnataka, India
 

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Cloud computing is a technology where IT-related resources are dynamically provided "as a service" to the customers through Internet. The customer can demand for the services dynamically from Cloud Service Provider (CSP)s, take them on lease based on Service Level Agreement (SLA), release the resources after completion of task and pay for what is used. The required services may not be available from a single CSP. There are many CSPs providing multiple services with different Quality of Service (QoS). The customer has to discover the available services with the expected QoS which is one of the major challenges to be solved in cloud computing today. In this paper we dynamically create a repository of the cloud services and aggregate them whenever there is a demand for service and then derive that the services obtained from the repository are time efficient as compared to direct service discovery.

Keywords

Repository, Aggregation.
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  • Altmann, J., & Rana, O. F., (2010). GECON2010, LNCS 6296, pp. 16-33, 2010. Springer-Verlag Berlin Heidelberg.

  • Buyya, R., Ranjan, R., Calheiros, R. N. (2010). Intercloud: Utility oriented federation of cloud computing environments for scaling of application services. Proceedings of the 10th International Conference on Algorithms and Architectures for Parallel Processing.

  • Cavalcante, E. (2012). Optimizing services selection in a cloud multiplatform scenario. Retrieved form http:// www.ppgsc.ufrn.br/~evertonrsc/publications/2012LatinCloud.pdf

  • Di Modica, G., Petralia, G., & Tomarchio, O. (2013). An SLA ontology to support service discovery in future cloud markets. 27th International Conference on Advanced Information Networking and Applications Workshops.

  • Garg, S. K., Versteeg, S., & Buyya, R. (2011). SMICloud: A Framework for Comparing and Ranking Cloud Services. 2011 Fourth IEEE Conference on Utility and Cloud Computing, (pp. 210-218).

  • Gartner. (2015). Gartner Research. Retrieved from http://www.gartner.com/technology/research/cloud-computing/ report/cloud-services-bokerage.jsp

  • Huang, C. J. (2013). An adaptive resource management scheme in cloud computing. Engineering Applications of Artificial Intelligence, 26(1), 382-389.

  • Jain, P., Rane, D., & Patidar, S. (2012). A novel cloud bursting brokerage and aggregation (CBBA) algorithm for multi cloud environment. 2nd International Conference on Advanced Computing & Communication Technologies (ACCT).

  • Kousiouris, G., Kyriazis, D. P., Varvarigou, T., Oliveros, E., & Mandic, P. (2011). Taxonomy and state of the art of service discovery mechanisms and their relation to the cloud computing stack cloud computing stack.

  • Leyli, M. & Jahani, A. (2014). Ranking approaches for cloud computing services based on QoS: A review. ARPN Journal of Systems and Software, March, 4(2), 50-58.

  • Li, B., Cao, B., Wen, K., & Li, R. (2011). Trustworthy assurance of service interoperation in cloud environment. International Journal of Automation and Computing, 8(3), 297- 308.

  • Manvi, S. S., & Shyam, G. K. (2014). Resource management for infrastructure as a Service (IAAS) in cloud computing: A survey. Journal of Network and Computer Applications, 41, 424-440.

  • Mattess, M. (2011). Cloud bursting: Managing peak loads by leasing public cloud services. Cloud Computing: Methodology, Systems, and Applications, CRC Press, USA.

  • Maurer, M., Brandic, I., & Sakellariou, R. (2013). Adaptive resource configuration for cloudinfrastructure management. Future Generation Computer Systems, 29(2), 472-487.

  • Mındruta, C., & Fortis, T. F. (2013). A semantic registry for cloud services. 27th International Conference on Advanced Information Networking and Applications Workshops.

  • Miranda, Z. (2012). An ontology based system for cloud infrastructure services discovery. Retrieved from http://arxiv.org/ftp/arxiv/papers/1212/1212.0156.pdf

  • Modica, G. D., Petralia, G., & Tomarchio, O. (2012). A business ontology to enable semantic matchmaking in open cloud markets. International Conference on Semantics, Knowledge and Grid.

  • Nair, S. K. (2010). Towards secure cloud bursting, brokerage and aggregation. IEEE 8th European Conference on Web Services (ECOWS).

  • Qi, Z., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges, The Brazilian Computer Society.

  • Qu, L., Wang, Y., & Orgun, M. A. (2013). Cloud service selection based on aggregation of user feedback and qualitative performance assessment. Proceedings of 2013 IEEE International Conference on Services and Computing, pp. 52-159.

  • Ranjan, R., Buyya, R., & Benatallah, B. (2012). Special section: software architectures and application development environments for Cloud computing. Software- Practice and Experience, 42, 391-394.

  • Taekgyeong, H., & Kwang, M. S. (2010). An Ontology enhanced Cloud Service Discovery. Retrieved from www.iaeng.org/publications/IMECS2010_pp644649.pdf

  • Toosi, A. N., Calheiros, R. N., & Buyya, R. (2014). Interconnected cloud computing environments: Challenges, taxonomy and survey. Journal ACM Computing Surveys (CSUR), July, 47(1).

  • Tserpes, K., Aisopos, F., Kyriazis, D., & Varvarigou, T. (2010). Service selection decision support in the internet of services. Economics of Grids Clouds, Systems and Services, 6296, 16-33. Springer Berlin Heidelberg.

  • Vouk, M. A. (2008). Cloud computing- Issues, research and implementations. Journal of Computing and Information Technology, 16(4), 235-246.

  • Zhang, J., He, L., Feiyi, H. & Bin, L. (2013). Service discovery architecture applied in cloud computing environments. Applied Mechanics and Materials, 241- 244, 3177-3183.

  • Zhang, M., Ranjan, R., Haller, A., Georgakopoulos, D., Menzel, M., & Nepal, S. (2012). An ontologybased system for Cloud infrastructure services’ discovery. In Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2012 8th International Conference on (pp. 524-530).


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  • Automation of Discovery and Aggregation of Cloud Services

Abstract Views: 440  |  PDF Views: 242

Authors

Sreedevi R. Nagarmunoli
Department of Computer Science and Engineering, KLE DR MSS College of Engineering and Technology, Belaeavi, Karnataka, India
Nandini S. Sidnal
Department of Computer Science and Engineering, KLE DR MSS College of Engineering and Technology, Belaeavi, Karnataka, India

Abstract


Cloud computing is a technology where IT-related resources are dynamically provided "as a service" to the customers through Internet. The customer can demand for the services dynamically from Cloud Service Provider (CSP)s, take them on lease based on Service Level Agreement (SLA), release the resources after completion of task and pay for what is used. The required services may not be available from a single CSP. There are many CSPs providing multiple services with different Quality of Service (QoS). The customer has to discover the available services with the expected QoS which is one of the major challenges to be solved in cloud computing today. In this paper we dynamically create a repository of the cloud services and aggregate them whenever there is a demand for service and then derive that the services obtained from the repository are time efficient as compared to direct service discovery.

Keywords


Repository, Aggregation.

References