Semantic Similarity Model for Risk Assessment in Forming Cloud Computing SLAs
Cloud computing has enabled users to access various resources and applications as a service and in return pay the provider only for the time for which they are used. Service Level Agreements (SLA) are formed between the user and provider to ensure that the required services and applications are delivered as expected. With the increase of public cloud providers, challenges such as availability, reliability, security, privacy and transactional risk demand detailed assessment during the formation of SLAs. This paper focuses on one sub-category of transactional risk while forming SLAs: namely, performance risk. We argue that performance risk assessment should be done by the user before entering into an SLA with a service provider. We propose to measure performance risk according to the specific context and assessment criteria with the aid of a semantic similarity model for the SLA requirement being negotiated in a cloud computing environment. We show through simulations that the performance risk analysis is more accurate using semantic similarity matching compared with analysis without semantic similarity matching.
KeywordsPerformance Risk Service Level Agreement Cloud Computing Context Assessment Criteria Semantic Similarity Model
Unable to display preview. Download preview PDF.
- 2.Comellas, J.O.F., Presa, I.G., Fernández, J.G.: SLA-driven Elastic Cloud Hosting Provider. In: Proceedings of the 18th Euromicro Conference on Parallel, Distributed and Network-based Processing, pp. 111–118. IEEE Computer Society, Pisa (2010)Google Scholar
- 5.Dillon, T., Wu, C., Chang, E.: Cloud Computing: Issues and Challenges. In: Proceedings on the 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 27–33. IEEE Computer Society, Perth (2010)Google Scholar
- 6.Fitó, J.O., Guitart, J.: Introducing Risk Management into Cloud Computing. Barcelona Supercomputing Center and Technical University of Catalonia, Barcelona, Spain (2010)Google Scholar
- 7.AssessGrid Consortium.: D4.1 Advanced Risk Assessment. In: Carlsson, C., Weissmann, O. (eds.): Assess Grid Deliverable (2008)Google Scholar
- 8.ISO Guide 73: Risk Management Vocabulary (2009), http://www.iso.org/iso/cataloguedetail?csnumber=44651
- 9.ISO 31000: Risk management - Principles and guidelines (2009), http://www.iso.org/iso/cataloguedetail?csnumber=43170
- 10.Aberer, K., Despotovic, Z.: Managing trust in a Peer-2-Peer Information System. In: ACM (ed.): Proceedings of the Tenth International Conference on Information and Knowledge Management (CIKM 2001), Atlanta, Georgia, USA, pp. 310–317 (2001) Google Scholar
- 18.Sowa, J.F.: Semantic Networks. In: Shapiro, S.C. (ed.) Encyclopaedia of Artificial Intelligence. Wiley, Chichester (1992)Google Scholar
- 19.Dong, H., Hussain, F.K., Chang, E.: A hybrid concept similarity measure model for ontology environment. In: Meersman, R., Herrero, P., Dillon, T. (eds.) OTM 2009, pp. 848–857. Springer, Vilamoura (2009)Google Scholar
- 21.Dong, H., Hussain, F.K., Chang, E.: A context-aware semantic similarity model for ontology environments. Concurrency and Computation: Practice and Experience (in Press) Google Scholar