ABSTRACT
SLA is a contract between service providers and consumers, mandating specific numerical target values which the service needs to achieve. For service providers, detecting and preventing SLA violation becomes very important to enhance customer trust and avoid penalty charges. Therefore, it is necessary for providers to detect and forecast possible service violations. However, this is difficult to achieve when dealing with service violation in a cloud-based system due to multiple Quality of Service (QoS) parameters. In this work, manifold learning is used to reduce the high dimensionality problem arising from multiple QoS data into 1-D output of violation level (low, medium, high) data. From the transformed data, service violation will be detected as well as predicted based on violation level data. The violation level is obtained from aggregate value of each QoS weightage. Based on QoS data of 14 days, manifold learning is able to scale down 5 various parameters into a single parameter before detection and prediction process is performed. The prediction accuracy of Support Vector Regression as the time series analysis technique used is able to achieve 80%.
- Tenenbaum, J.B., de Sliva, V. and Landford, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science. vol. 290 (Dec 2000), pp. 2319--2323. "Google Scholar
- Roweis, S.T. and Saul, L. K. 2000. Nonlinear dimensionality reduction by local linear embedding. Science. vol. 290 (Dec 2000), pp. 2323--2326.Google Scholar
- Belkin, M. and Niyogi, P. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, vol. 15 (June 2003), no 6, pp. 1373--1396. Google ScholarDigital Library
- Donoho, D. and Grimes, C. 2003. Hessian eigenmaps: new locally linear embedding techniques for high-dimensional data. In Proc. Nat. Acad. Sci. USA, vol. 100, no. 10, pp. 5591--5596.Google ScholarCross Ref
- Zhang, Z. and Zha, H. 2004. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. (2004), vol. 26, pp. 313--338. Google ScholarDigital Library
- Patwari, N., Hero, A.O.III, and Pacholski, A. 2005. Manifold learning visualization of network traffic data. In Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data (MineNet '05). ACM, New York, NY, USA, 191--196. Google ScholarDigital Library
- Emeakaroha, V.C., Netto, M. A. S., Calheiros, R. N., Brandic, I., Buyya, R. and De Rose, C. A. F. 2012. Towards Autonomic Detection of SLA Violations in Cloud Infrastructures. Future Generation Computer Systems. Vol. 28, Issue 7, July 2012, pages 1017 -- 1029. Google ScholarDigital Library
- Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., and Leymann, F. 2010. Runtime prediction of service level agreement violations for composite services. In Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops, pp. 176--186. Google ScholarDigital Library
- Lorenzoli, D. and Spanoudakis, G. 2010.EVEREST+: Run-Time SLA Violations Prediction. In MW4SOC'10, November. Google ScholarDigital Library
- Michlmayr, A., Rosenberg, F., Leitner, P. and Dustdar, D. 2009. Comprehensive QoS monitoring of Web services and event-based SLA violation detection. In Proceedings of the 4th MWSOC, ACM. Google ScholarDigital Library
- Bodenstaff, L., Wombacher, A., Reichert, M. and Jaeger, M.C. 2008. Monitoring dependencies for SLAs: the MoDe4SLA approach. In Proceedings of the 2008 IEEE International Conference on Services Computing (SCC'08), pp. 21--29, IEEE Computer Society, Washington, DC. Google ScholarDigital Library
- Bodenstaff, L., Wombacher, A., Reichert, M. and Jaeger, M. C. 2009. Analyzing impact factors on composite services. In Proceedings of the 2009 IEEE International Conference on Services Computing (SCC'09), pp. 218--226, IEEE Computer Society, Los Alamitos. Google ScholarDigital Library
- Weinberger, K.; Packer, B.; and Saul, L. 2005. Nonlinear dimensionality reduction by semi-definite programming and kernel matrix factorization. In Proceedings of the tenth international workshop on artificial intelligence and statistics, 381--388.Google Scholar
- Hoppe, H., DeRose, T., Duchamp, T., McDonald, J. and Stuetzle, W. 1994. Surface reconstruction from unorganized points. University of Washington.Google Scholar
- Fadzil, A. M. H., Paputungan, I. V. and Fadzil, M. H. 2013. Support Vector regression for Service Level Agreement violation prediction. In Proc. Computer, Control, Informatics and Its Applications (IC3INA), Jakarta, pp. 307--311.Google Scholar
Index Terms
- Manifold learning in SLA violation detection and prediction for cloud-based system
Recommendations
Monitoring and Management of Service Level Agreements in Cloud Computing
ICCAC '15: Proceedings of the 2015 International Conference on Cloud and Autonomic ComputingCloud computing environment consists of various interactive entities like cloud service providers, cloud service brokers, cloud customers and end-users with different objectives and expectations. Service Level Agreements (SLAs) manage the relationship ...
A Conceptual Platform of SLA in Cloud Computing
DASC '11: Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure ComputingCloud computing is a promising technology, where the infrastructure, developing platform, software and storage are delivered as a service. With the development of cloud computing, more and more cloud service providers emerge. However, there are no ...
Monitoring, Prediction and Prevention of SLA Violations in Composite Services
ICWS '10: Proceedings of the 2010 IEEE International Conference on Web ServicesWe propose the PREvent framework, which is a system that integrates event-based monitoring, prediction of SLA violations using machine learning techniques, and automated runtime prevention of those violations by triggering adaptation actions in service ...
Comments