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A Bayesian approach to parameter inference in queueing networks. (English) Zbl 1369.62050


MSC:

62F15 Bayesian inference
60K25 Queueing theory (aspects of probability theory)
68M20 Performance evaluation, queueing, and scheduling in the context of computer systems

Software:

BayesDA; MASCOT

References:

[1] C. Armero and M. J. Bayarri. 1994. Bayesian prediction in M/M/1 queues. Queueing Systems 15, 1 (1975), 401–417. · Zbl 0789.60072
[2] S. Asmussen and P. W. Glynn. 2007. Stochastic Simulation: Algorithms and Analysis. Springer Science & Business Media. · Zbl 1126.65001
[3] F. Baskett, K. M. Chandy, R. R. Muntz, and F. G. Palacios. 1975. Open, closed, and mixed networks of queues with different classes of customers. Journal of the ACM 22, 2 (1975), 248–260. · Zbl 0313.68055 · doi:10.1145/321879.321887
[4] G. Bolch, S. Greiner, H. de Meer, and K. S. Trivedi. 2006. Queueing Networks and Markov Chains. 2nd ed., John Wiley and Sons. · Zbl 1099.60002 · doi:10.1002/0471791571
[5] S. Brooks, A. Gelman, G. Jones, and X. Meng. 2011. Handbook of Markov Chain Monte Carlo. CRC Press. · Zbl 1218.65001 · doi:10.1201/b10905
[6] G. Casale. 2006. An efficient algorithm for the exact analysis of multiclass queueing networks with large population sizes. In Proc. of Joint ACM SIGMETRICS/IFIP Performance. ACM Press, 169–180. · doi:10.1145/1140103.1140298
[7] G. Casale, P. Cremonesi, and R. Turrin. 2008. Robust workload estimation in queueing network performance models. In Proc. of Euromicro PDP. 183–187. · doi:10.1109/PDP.2008.80
[8] P. Cremonesi, K. Dhyani, and A. Sansottera. 2010. Service time estimation with a refinement enhanced hybrid clustering algorithm. Proc. of ASMTA. 291–305. · doi:10.1007/978-3-642-13568-2_21
[9] P. Cremonesi and A. Sansottera. 2012. Indirect estimation of service demands in the presence of structural changes. In Proc. of QEST. IEEE, 249–259. · doi:10.1109/QEST.2012.18
[10] P. Cremonesi, P. J. Schweitzer, and G. Serazzi. 2002. A unifying framework for the approximate solution of closed multiclass queuing networks. IEEE Transactions on Computers 51 (2002), 1423–1434. · doi:10.1109/TC.2002.1146708
[11] P. Damlen, J. C. Wakefield, and S. G. Walker. 1999. Gibbs sampling for Bayesian nonconjugate and hierarchical models by using auxiliary variables. Journal of the Royal Statistical Society, B 61 (1999), 331–344. · Zbl 0913.62028 · doi:10.1111/1467-9868.00179
[12] E. de Sousa e Silva and R. R. Muntz. 1988. Simple relationships among moments of queue lengths in product form queueing networks. IEEE Transactions on Computers 37, 9 (1988), 1125–1129.
[13] S. Geman and D. Geman. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (1984), 721–741. · Zbl 0573.62030 · doi:10.1109/TPAMI.1984.4767596
[14] A. Gelman, J. Carlin, H. Stern, and D. Rubin. 2014. Bayesian Data Analysis. Taylor & Francis. · Zbl 1279.62004
[15] W. Hastings. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 1 (1970), 97–109. · Zbl 0219.65008 · doi:10.1093/biomet/57.1.97
[16] D. R. Insua, M. Wiper, and F. Ruggeri. 1998. Bayesian analysis of M/Er/1 and M/H_k/1 queues. Queueing Systems 30, 3 (1998), 289–308. · Zbl 0917.90137 · doi:10.1023/A:1019173206509
[17] G. Koole and A. Mandelbaum. 2012. Queueing models of call centers: An introduction. Annals of Operations Research 113, 1 (2002), 41–59. · Zbl 1013.90090 · doi:10.1023/A:1020949626017
[18] A. Kalbasi, D. Krishnamurthy, J. Rolia, and S. Dawson. 2012. DEC: Service demand estimation with confidence. IEEE Transactions on Software Engineering 38, 3 (2012), 561–578. · doi:10.1109/TSE.2011.23
[19] A. Kalbasi, D. Krishnamurthy, J. Rolia, and M. Richter. 2011. MODE: Mix driven on-line resource demand estimation. In Proc. of CNSM. International Federation for Information Processing, 1–9.
[20] C. Knessl and C. Tier. 1992. Asymptotic expansions for large closed queueing networks with multiple job classes. IEEE Transactions on Computers 41, 4 (1992), 480–488. · doi:10.1109/12.135560
[21] S. Kraft, S. Pacheco-Sanchez, G. Casale, and S. Dawson. 2009. Estimating service resource consumption from response time measurements. In Proc. of Valuetools. ACM, 48. · doi:10.4108/ICST.VALUETOOLS2009.7526
[22] S. Lam. 1982. Dynamic scaling and growth behavior of queuing network normalization constants. Journal of the ACM 29, 2 (1982), 492–513. · Zbl 0497.60095 · doi:10.1145/322307.322321
[23] Y. Liu, I. Gorton, and A. Fekete. 2005. Design-level performance prediction of component-based applications. IEEE Transactions on Software Engineering 31, 11 (2005), 928–941. · doi:10.1109/TSE.2005.127
[24] Z. Liu, L. Wynter, C. Xia, and F. Zhang. 2006. Parameter inference of queueing models for IT systems using end-to-end measurements. Performance Evaluation 63, 1 (2006), 36–60. · doi:10.1016/j.peva.2004.12.001
[25] N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 6 (1953), 1087–1092. · doi:10.1063/1.1699114
[26] T. Minka. 2000. Estimating a Dirichlet distribution. Technical report, MIT.
[27] J. Moschetta and G. Casale. 2012. OFBench: An enterprise application benchmark for cloud resource management studies. In Proc. of SYNASC. IEEE, 393–399. · doi:10.1109/SYNASC.2012.39
[28] R. Neal. 2003. Slice sampling. Annals of Statistics (2003), 705–741. · Zbl 1051.65007 · doi:10.1214/aos/1056562461
[29] G. Pacifici, W. Segmuller, M. Spreitzer, and A. Tantawi. 2008. Cpu demand for web serving: Measurement analysis and dynamic estimation. Performance Evaluation 65, 6 (2008), 531–553. · doi:10.1016/j.peva.2007.12.001
[30] J. Perez, S. Pacheco-Sanchez, and G. Casale. 2013. An offline demand estimation method for multi-threaded applications. In Proc. of MASCOTS. IEEE Computer Society, 21–30. · doi:10.1109/MASCOTS.2013.10
[31] Apache OFBiz project. 2014. http://ofbiz.apache.org.
[32] J. Rolia and V. Vetland. 1995. Parameter estimation for performance models of distributed application systems. In Proc. of CASCON. IBM Press, 54.
[33] J. Rolia and V. Vetland. 1998. Correlating resource demand information with ARM data for application services. In Proc. of WOSP. ACM, 219–230. · doi:10.1145/287318.287366
[34] J. Rolia and K. Sevcik. 1995. The method of layers. IEEE Transactions on Software Engineering 21, 8 (Aug. 1995), 689–700. · doi:10.1109/32.403785
[35] J. Ross, T. Taimre, and P. Pollett. 2007. Estimation for queues from queue length data. Queueing Systems 55, 2 (2007), 131–138. · Zbl 1178.90102 · doi:10.1007/s11134-006-9009-2
[36] K. Ross, D. Tsang, and J. Wang. 1994. Monte Carlo summation and integration applied to multiclass queuing networks. Journal of the ACM 41, 6 (1994), 1110–1135. · Zbl 0829.68010 · doi:10.1145/195613.195630
[37] K. Ross and J. Wang. 1997. Implementation of Monte Carlo integration for the analysis of product-form queueing networks. Performance Evaluation 29, 4 (1997), 273–292. · doi:10.1016/S0166-5316(96)00051-X
[38] A. Sharma, R. Bhagwan, M. Choudhury, L. Golubchik, R. Govindan, and G. Voelker. 2008. Automatic request categorization in internet services. Performance Evaluation Review 36, 2 (2008), 16–25. · doi:10.1145/1453175.1453179
[39] S. Spinner, G. Casale, F. Brosig, and S. Kounev. 2015. Evaluating approaches to resource demand estimation. Performance Evaluation 92, 10 (2015), 51–71. · doi:10.1016/j.peva.2015.07.005
[40] C. Sutton and M. Jordan. 2011. Bayesian inference for queueing networks and modeling of internet services. Annals of Applied Statistics 5, 1 (2011), 254–282. · Zbl 1220.62024 · doi:10.1214/10-AOAS392
[41] B. Tuffin. 1997. Variance reduction applied to product form multiclass queuing networks. ACM Transactions on Modeling and Computer Simulation 7, 4 (1997), 478–500. · Zbl 0917.65129 · doi:10.1145/268403.268419
[42] B. Urgaonkar, G. Pacifici, P. J. Shenoy, M. Spreitzer, and A. N. Tantawi. 2005. An analytical model for multi-tier internet services and its applications. In Proc. of ACM SIGMETRICS. ACM Press, 291–302. · doi:10.1145/1071690.1064252
[43] W. Wang and G. Casale. 2013. Bayesian service demand estimation using gibbs sampling. In Proc. of IEEE MASCOTS. IEEE Press, 567–576. · doi:10.1109/MASCOTS.2013.78
[44] X. Wu and M. Woodside. 2008. A calibration framework for capturing and calibrating software performance models. In Proc. of EPEW. Springer, 32–47. · doi:10.1007/978-3-540-87412-6_4
[45] Q. Zhang, L. Cherkasova, and E. Smirni. 2007. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In Proc. of ICAC. 27–27. · doi:10.1109/ICAC.2007.1
[46] T. Zheng, C. Woodside, and M. Litoiu. 2008. Performance model estimation and tracking using optimal filters. IEEE Transactions on Software Engineering 34, 3 (2008), 391–406. · doi:10.1109/TSE.2008.30
[47] T. Zheng, J. Yang, M. Woodside, M. Litoiu, and G. Iszlai. 2005. Tracking time-varying parameters in software systems with extended Kalman filters. In Proc. of CASCON. IBM Press, 334–345.
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