Abstract
A service level agreement (SLA) is a legal document that binds consumers and providers together for the delivery of specific services for a certain period of time. Providers need a viable SLA to maintain successful relationships with consumers. A viable SLA, based on the previous profile of a consumer, will help a service provider determine whether to accept or reject a consumer’s request and the amount of resources to offer them. In this paper we propose a soft-computing based approach to form a personalized and viable SLA. This process is carried out in the pre-interaction time phase. We build a Fuzzy Inference System (FIS) and consider a consumer’s reliability value and contract duration as the input factors to determine the amount of resources to offer to the consumer. In addition to the Fuzzy Inference System, we tested various Neural Network-based methods for viable SLA formation and compared their prediction accuracy with the output of the FIS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hussain, O.K., et al.: Risk assessment and management in the networked economy. Vol. 412. Springer, Heidelberg (2012)
Hussain, W., Hussain, F.K., Hussain, O.K.: Maintaining Trust in Cloud Computing through SLA Monitoring. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds.) ICONIP 2014, Part III. LNCS, vol. 8836, pp. 690–697. Springer, Heidelberg (2014)
Oliveira, A.C., et al.: Efficient network service level agreement monitoring for cloud computing systems. In: 2014 IEEE Symposium on Computers and Communication (ISCC). IEEE (2014)
Lu, K., et al.: Fault-tolerant Service Level Agreement lifecycle management in clouds using actor system. Future Generation Computer Systems (2015). https://scholar.google.com.au/scholar?hl=en&q=Fault-tolerant+Service+Level+Agreement+lifecycle+management+in+clouds+using+actor+system&btnG=&as_sdt=1%2C5&as_sdtp=
Tang, B., Tang, M.: Bayesian model-based prediction of service level agreement violations for cloud services. In: Theoretical Aspects of Software Engineering Conference (TASE). IEEE (2014)
Hussain, W., F.K. Hussain, and O.K. Hussain. Comparative Analysis of Consumer Profile-based Methods to Predict SLA Violation. in IEEE International Conference on Fuzzy Systems: Istanbul. IEEE, Turkey (2015)
Tan, W., Li, L., Sun, Y.: A Novel Performance Prediction Framework for Web Service Workflow Applications. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds.) HCC 2014. LNCS, vol. 8944, pp. 55–68. Springer, Heidelberg (2015)
Ron, S., Aliko, P.: Service level agreements. Internet NG. Internet NG project (1999-2001) http://ing. ctit.utwente.nl/WU2, 2001
Wustenhoff, E., BluePrints, S.: Service level agreement in the data center. Sun Microsystems Professional Series (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hussain, W., Hussain, F.K., Hussain, O.K. (2015). Towards Soft Computing Approaches for Formulating Viable Service Level Agreements in Cloud. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-26561-2_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26560-5
Online ISBN: 978-3-319-26561-2
eBook Packages: Computer ScienceComputer Science (R0)