Abstract
In this paper we study the impact of different types of constraints on the maximum throughput that a system can handle. In particular, we focus on constraints limiting the use of resources and/or the allowed response time. The problem is made even more difficult by the pronounced diversity in resource requirements of the different applications in execution, i.e., by the multiclass characteristic of the workloads. The proposed approach allows to determine the maximum load of the different classes, while still satisfying the considered performance objectives. An experimental validation of the described technique through the study of a realistic e-commerce application is presented.
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
Preview
Unable to display preview. Download preview PDF.
References
Anselmi, J., Casale, G.: Heavy-traffic revenue maximization in parallel multiclass queues. Perform. Eval. 70(10), 806–821 (2013), http://dx.doi.org/10.1016/j.peva.2013.08.008
Anselmi, J., Cremonesi, P., Amaldi, E.: On the consolidation of data-centers with performance constraints. In: Mirandola, R., Gorton, I., Hofmeister, C. (eds.) QoSA 2009. LNCS, vol. 5581, pp. 163–176. Springer, Heidelberg (2009)
Ayoub, R., Ogras, U., Gorbatov, E., Jin, Y., Kam, T., Diefenbaugh, P., Rosing, T.: Os-level power minimization under tight performance constraints in general purpose systems. In: 2011 International Symposium on Low Power Electronics and Design (ISLPED), pp. 321–326 (2011)
Balbo, G., Serazzi, G.: Asymptotic analysis of multiclass closed queueing networks: Multiple bottlenecks. Performance Evaluation 30(3), 115–152 (1997)
Barford, P., Crovella, M.: Generating representative web workloads for network and server performance evaluation. SIGMETRICS Perform. Eval. Rev. 26, 151–160 (1998), http://doi.acm.org/10.1145/277858.277897
Benchmark, R.: http://rubis.ow2.org/
Casale, G., Serazzi, G.: Bottlenecks identification in multiclass queueing networks using convex polytopes. In: Proc. of IEEE MASCOTS Symposium, pp. 223–230. IEEE Press (2004)
Cherkasova, L., Phaal, P.: Session-based admission control: A mechanism for peak load management of commercial web sites. IEEE Trans. Comput. 51, 669–685 (2002), http://dx.doi.org/10.1109/TC.2002.1009151
Eager, D.L., Sevcik, K.C.: Bound hierarchies for multiple-class queuing networks. J. ACM 33, 179–206 (1986), http://doi.acm.org/10.1145/4904.4992
Elnikety, S., Tracey, J., Nahum, E., Zwaenepoel, W.: A method for transparent admission control and request scheduling in e-commerce web sites. In: Proceedings of the 13th WWW, pp. 276–286 (2004)
Leitner, P., Michlmayr, A., Rosenberg, F., Dustdar, S.: Monitoring, prediction and prevention of sla violations in composite services. In: ICWS 2010, pp. 369–376 (July 2010)
Litoiu, M.: A performance analysis method for autonomic computing systems. ACM Trans. Auton. Adapt. Syst. 2 (March 2007), http://doi.acm.org/10.1145/1216895.1216898
Liu, Z., Squillante, M.S., Wolf, J.L.: On maximizing service-level-agreement profits. In: Proceedings of EC 2001, pp. 213–223. ACM, New York (2001), http://doi.acm.org/10.1145/501158.501185
Mars, J., Tang, L.: Whare-map: Heterogeneity in “homogeneous” warehouse-scale computers. In: ISCA 2013, ACM, New York (2013), http://doi.acm.org/10.1145/2485922.2485975
Menascé, D.A., Almeida, V.A.F., Fonseca, R., Mendes, M.A.: Business-oriented resource management policies for e-commerce servers. Perform. Eval. 42, 223–239 (2000), http://dx.doi.org/10.1016/S0166-53160000034-1
Nocedal, J., Wright, S.J.: Numerical optimization. Springer, New York (2006), http://site.ebrary.com/id/10228772
Octave, G.: http://www.gnu.org/software/octave/
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C: The art of scientific computing, 2nd edn. Cambridge University Press, New York (1992)
Singh, R., Sharma, U., Cecchet, E., Shenoy, P.: Autonomic mix-aware provisioning for non-stationary data center workloads. In: ICAC 2010, pp. 21–30. ACM, New York (2010), http://doi.acm.org/10.1145/1809049.1809053
Urgaonkar, B., Chandra, A.: Dynamic provisioning of multi-tier internet applications. In: Proceedings of the Second International Conference on Automatic Computing, USA, pp. 217–228. IEEE Computer Society, Washington, DC (2005), http://dx.doi.org/10.1109/ICAC.2005.27
Villela, D., Pradhan, P., Rubenstein, D.: Provisioning servers in the application tier for e-commerce systems. ACM Trans. Internet Technol. 7(1) (February 2007), http://doi.acm.org/10.1145/1189740.1189747
Walsh, W.E., Tesauro, G., Kephart, J.O., Das, R.: Utility functions in autonomic systems. In: Proceedings of the International Conference on Autonomic Computing, pp. 70–77 (2004)
website, T.A.E, http://aws.amazon.com/ec2/#instance
Website, T.I.S.C, http://www.ibm.com/cloud-computing/us/en/
Website, T.M.A, http://www.microsoft.com/windowsazure/
Xu, X., Yu, H., Cong, X.: A qos-constrained resurce allocation game in federated cloud. In: IMIS 2013, pp. 268–275 (2013)
Xue, J.W.J., Chester, A.P., He, L., Jarvis, S.A.: Model-driven server allocation in distributed enterprise systems. In: ABIS 2009 (2009)
Zhou, X., Ippoliti, D.: Resource allocation optimization for quantitative service differentiation on server clusters. Journal of Parallel and Distributed Computing 68(9), 1250–1262 (2008), http://www.sciencedirect.com/science/article/pii/S074373150800107X
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Cerotti, D., Gribaudo, M., Krüger, I., Piazzolla, P., Seracini, F., Serazzi, G. (2014). Throughput Maximization with Multiclass Workloads and Resource Constraints. In: Sericola, B., Telek, M., Horváth, G. (eds) Analytical and Stochastic Modeling Techniques and Applications. ASMTA 2014. Lecture Notes in Computer Science, vol 8499. Springer, Cham. https://doi.org/10.1007/978-3-319-08219-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-08219-6_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08218-9
Online ISBN: 978-3-319-08219-6
eBook Packages: Computer ScienceComputer Science (R0)