Abstract
We develop an online optimisation framework for self tuning of computer systems. Towards this, we first discuss suitable objective functions. We then develop an iterative technique that is robust to noisy measurements of objective function and also requires fewer perturbations on the configuration. We essentially adapt the Nelder-Mead algorithm to work with constrained variables and also allow noisy measurements. Extensive experimental results on a queueing model and on an actual system illustrate the performance of our scheme.
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Kleinrock, L.: Power and deterministic rules of thumb for probabilistic problems in computer communications. In: Proceedings of the International Conference on Communications (June 1979)
Jain, R., Chiu, D.M., Hawe, W.: A quantitative measure of fairness and discrimination for resource allocation in shared systems. Technical Report DEC TR-301, Digital Equipment Corporation, Littleton, MA (1984)
Gandhi, N., Tilbury, D.M., Parekh, S., Hellerstein, J.: Feedback control of a lotus notes server: Modeling and control design. In: Proceedings of the American Control Conference, pp. 3000–3005 (2001)
Diao, Y., Hellerstein, J.L., Parekh, S.: MIMO control of an Apache web server: Modelling and controller design. In: Proceedings of the American Control Conference, pp. 4922–4927 (2002)
Kamra, A., Misra, V., Nahum, E.M.: Yaksha: A self-tuning controller for managing the performance of 3-tiered web sites. In: Proceedings of IEEE International Workshop on Quality of Service (IWQOS), June 2004, pp. 47–56 (2004)
Diao, Y., Eskesen, F., Froehlich, S., Hellerstein, J.L., Spainhower, L.F., Surendra, M.: Generic online optimization of multiple configuration parameters with application to a database server. In: Brunner, M., Keller, A. (eds.) DSOM 2003. LNCS, vol. 2867, pp. 3–15. Springer, Heidelberg (2003)
Olsson, D.M., Nelson, L.S.: The nelder-mead simplex procedure for function minimization. Technometrics 17(1), 45–51 (1975)
Xiong, Q., Jutan, A.: Continuous optimization using a dynamic simplex method. Chemical Engineering Science 58(16), 3817–3828 (2003)
Bagchi, S., Das, R., Diao, Y., Kaplan, M.A., Kephart, J.O.: Dynamic online multi-parameter optimization system and method for autonomic computing systems, US Patent Application 20080221858
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Poojary, S., Raghavendra, R., Manjunath, D. (2010). An Online, Derivative-Free Optimization Approach to Auto-tuning of Computing Systems. In: Kant, K., Pemmaraju, S.V., Sivalingam, K.M., Wu, J. (eds) Distributed Computing and Networking. ICDCN 2010. Lecture Notes in Computer Science, vol 5935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11322-2_41
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DOI: https://doi.org/10.1007/978-3-642-11322-2_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11321-5
Online ISBN: 978-3-642-11322-2
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