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Issue No.04 - July-Aug. (2013 vol.17)
pp: 40-47
Alessio Gambi , Technical University of Vienna, Austria
Giovanni Toffetti , IBM Haifa
Cesare Pautasso , University of Lugano, Switzerland
Mauro Pezze , University of Milano Bicocca, Italy
The infrastructure-as-a-service paradigm for cloud computing lets service providers execute applications on third-party infrastructures with a pay-as-you-go billing model. Providers can balance operational costs and quality of service by monitoring application behavior and changing the deployed configuration at runtime as operating conditions change. Current approaches for automatically scaling cloud applications exploit user-defined rules that respond well to predictable events but don't react adequately to unexpected execution conditions. The authors' autonomic controllers, designed using Kriging models, automatically adapt to unpredicted conditions by dynamically updating a model of the system's behavior.
Computational modeling, Quality of service, Cloud computing, Data models, Service-oriented architecture, Analytical models, Internet, Elasticity, elasticity, cloud computing, infrastructure as a service, IaaS, autonomic controllers, Kriging models, self-adaptive controllers
Alessio Gambi, Giovanni Toffetti, Cesare Pautasso, Mauro Pezze, "Kriging Controllers for Cloud Applications", IEEE Internet Computing, vol.17, no. 4, pp. 40-47, July-Aug. 2013, doi:10.1109/MIC.2012.142
1. P. Bodik et al., “Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters,” Proc. Conf. Hot Topics in Cloud Computing (HotCloud 09), Usenix Assoc., 2009, pp. 75–80.
2. J. Kephart and D. Chess, “The Vision of Autonomic Computing,” Computer, vol. 36, no. 1, 2003, pp. 41–50.
3. G. Jung et al., “Generating Adaptation Policies for Multitier Applications in Consolidated Server Environments,” Proc. Int'l Conf. Autonomic Computing and Communications (ICAC 08), IEEE CS, 2008, pp. 23–32.
4. H.C. Lim, S. Babu, and J.S. Chase, “Automated Control for Elastic Storage,” Proc. Int'l Conf. Autonomic Computing (ICAC 10), ACM, 2010, pp. 1–10.
5. G. Tesauro, “Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies,” IEEE Internet Computing, vol. 11, no. 1, 2007, pp. 22–30.
6. B. Urgaonkar and A. Chandra, “Dynamic Provisioning of Multitier Internet Applications,” Proc. Int'l Conf. Autonomic Computing (ICAC 05), IEEE CS, 2005, pp. 217–228.
7. J.Z. Li et al., “Fast Scalable Optimization to Configure Service Systems having Cost and Quality of Service Constraints,” Proc. Int'l Conf. Autonomic Computing (ICAC 09), 2009, pp. 159–168.
8. M.N. Bennani and D.A. Menasce, “Resource Allocation for Autonomic Data Centers using Analytic Performance Models,” Proc. Int'l Conf. Autonomic Computing (ICAC 05), IEEE CS, 2005, pp. 229–240.
9. S.J. Malkowski et al., “Automated Control for Elastic n-Tier Workloads Based on Empirical Modeling,” Proc. 8th ACM Int'l Conf. Autonomic Computing (ICAC 11), ACM, 2011, pp. 131–140.
10. G. Toffetti et al., “Engineering Autonomic Controllers for Virtualized Web Applications,” Proc. Int'l Conf. Web Eng. (ICWE 10), Springer, 2010, pp. 66–80.
11. W. van Beers and J. Kleijnen, “Kriging Interpolation in Simulation: A Survey,” Proc. Winter Simulation Conf., IEEE, 2004, pp. 113–121.
12. D. Jones, M. Schonlau, and W. Welch, “Efficient Global Optimization of Expensive Black-Box Functions,” Global Optimization, vol. 13, no. 4, 1998, pp. 455–492.
13. A. Gambi, M. Pezzè, and G. Toffetti, “Protecting SLA with Surrogate Models,” Proc. Int'l Workshop Principles of Engineering Service-Oriented Systems (PESOS 10), ACM, 2010, pp. 71–77.
14. L. Rodero-Merino et al., “From Infrastructure Delivery to Service Management in Clouds,” Future Generation Computer Systems, vol. 26, no. 8, 2010, pp. 1226–1240.
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