Scoring Cloud Services Through Digital Ecosystem Community Analysis
- 988 Downloads
Cloud service selection is a complex process that requires assessment of not only individual features of a cloud service but also its ability to interoperate with an ecosystem of cloud services. In this position paper, we address the problem by devising metrics to measure the impact of interoperability among the cloud services to guide the cloud service selection process. We introduce concrete definitions and metrics to contribute to measuring the level of interoperability between cloud services. We also demonstrate a methodology to evaluate the metrics via a use case example. Our contributions prove that the proposed metrics cover critical aspects related to interoperability in multi-cloud arena and therefore form a robust baseline to compare cloud services in systematic decision making environments.
KeywordsCloud service Interoperability Multi-cloud Scoring Decision support
This work is partially supported by Secretaria de Universitats i Recerca of Generalitat de Catalunya (2014DI031) and conducted as a part of the MUSA project (Grant Agreement 644429) funded by the European Commission within call H2020-ICT-2014-1. Josep L. Larriba-Pey also thanks the Ministry of Economy and Competitivity of Spain and Generalitat de Catalunya, for grant numbers TIN2013-47008-R and SGR2014-890 respectively.
- 1.Topology and orchestration specification for cloud applications version 1.0. Technical report, OASIS Standard, Nov 2013Google Scholar
- 2.Gupta, S., Muntes-Mulero, V., Matthews, P., Dominiak, J., Omerovic, A., Aranda, J., Seycek, S.: Risk-driven framework for decision support in cloud service selection. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 545–554, May 2015Google Scholar
- 3.Han, S., Hassan, M., Yoon, C., Huh, E.: Efficient service recommendation system for cloud computing market. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, ICIS 2009, pp. 839–845. ACM, New York (2009)Google Scholar
- 4.Liu, F., Tong, J., Mao, J., Bohn, R., Messina, J., Badger, L., Leaf, D.: Nist cloud computing reference architecture. NIST Spec. Publ. 500, 292 (2011)Google Scholar
- 5.Muntes-Mulero, V., Matthews, P., Omerovic, A., Gunka, A.: Eliciting risk, quality and cost aspects in multi-cloud environments. In: The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization, CLOUD COMPUTING (2013)Google Scholar
- 6.Lie, Q., Yan, W., Orgun, M.A.: Cloud service selection based on the aggregation of user feedback and quantitative performance assessment. In: 2013 IEEE International Conference on Services Computing (SCC), pp. 152–159, June 2013Google Scholar
- 8.ur Rehman, Z., Hussain, O.K., Hussain, F.K.: Iaas cloud selection using MCDM methods. In: IEEE Ninth International Conference on e-Business Engineering (ICEBE), pp. 246–251, Sept 2012Google Scholar
- 9.ur Rehman, Z., Hussain, O.K., Chang, E., Dillon, T.: Decision-making framework for user-based inter-cloud service migration. Electronic Commerce Research and Applications (2015)Google Scholar
- 10.Uriarte, R.B., Tsaftaris, S., Tiezzi, F.: Service clustering for autonomic clouds using random forest. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 515–524, May 2015Google Scholar
- 11.Viveca, R., Meulen, W.: Gartner says worldwide public cloud services market is forecast to reach $204 billion in 2016. Technical report, Gartner Press Release (2016)Google Scholar