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Estimation and monitoring of traffic intensities with application to control of stochastic systems

dc.contributor.authorHung, Ying‐chaoen_US
dc.contributor.authorMichailidis, Georgeen_US
dc.contributor.authorChuang, Shih‐chungen_US
dc.date.accessioned2014-05-23T16:00:01Z
dc.date.available2015-05-04T14:37:25Zen_US
dc.date.issued2014-03en_US
dc.identifier.citationHung, Ying‐chao ; Michailidis, George; Chuang, Shih‐chung (2014). "Estimation and monitoring of traffic intensities with application to control of stochastic systems." Applied Stochastic Models in Business and Industry 30(2): 200-217.en_US
dc.identifier.issn1524-1904en_US
dc.identifier.issn1526-4025en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/106982
dc.publisherSpringeren_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherControl Policyen_US
dc.subject.otherStochastic Systemsen_US
dc.subject.otherControl Charten_US
dc.subject.otherEWMA Smootheren_US
dc.subject.otherTraffic Intensityen_US
dc.titleEstimation and monitoring of traffic intensities with application to control of stochastic systemsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/106982/1/asmb1961.pdf
dc.identifier.doi10.1002/asmb.1961en_US
dc.identifier.sourceApplied Stochastic Models in Business and Industryen_US
dc.identifier.citedreferenceGiurcanu M, Spokoiny V. Confidence estimation of the covariance function of stationary and locally stationary processes. Statistics and Decisions 2004; 22: 283 – 300. DOI: 10.1524/stnd.22.4.283.64315.en_US
dc.identifier.citedreferenceBjörklund E, Matinlauri I, Tierens A, Axelsson S, Forestier E, Jacobsson S, Ahlberg AJ, Kauric G, Mäntymaa P, Osnes L, Penttilä TL, Marquart H, Savolainen ER, Siitonen S, Torikka K, Mazur J, Porwit A. Quality control of flow cytometry data analysis for evaluation of minimal residual disease in bone marrow from acute leukemia patients during treatment. Journal of Pediatric Hematology/Oncology 2009; 31: 406 – 415. PMID: 19648789.en_US
dc.identifier.citedreferenceD'hautcourt JL. Quality control procedures for flow cytometric applications in the hematology laboratory. Hematology and Cell Therapy 1997; 38: 467 – 470. DOI: 10.1007/s00282‐996‐0467‐0.en_US
dc.identifier.citedreferenceSeamer LC, Kuckuck F, Sklar LA. Sheath fluid control to permit stable flow in rapid mix flow cytometry. Cytometry 1999; 35: 75 – 79.en_US
dc.identifier.citedreferenceBellizzi S, Bouc R. Adaptive sub‐optimal parametric control for non‐linear stochastic systems. Application to semi‐active isolators. Probability Methods in Applied Physics 1995; 451: 223 – 238. DOI: 10.1007/3‐540‐60214‐3_58.en_US
dc.identifier.citedreferenceCox DR. Prediction by exponentially weighted moving averages and related methods. Journal of the Royal Statistical Society. Series B 1961; 23: 414 – 442.en_US
dc.identifier.citedreferenceMontgomery DC, Mastrangelo CM. Some statistical process control methods for autocorrelated data. Journal of Quality Technology 1991; 23: 179 – 193.en_US
dc.identifier.citedreferenceRamjee R, Crato N, Ray BK. A note on moving average forecasts of long memory processes with an application to quality control. International Journal of Forecasting 2002; 18: 291 – 297. DOI: 10.1016/S0169‐2070(01)00159‐5.en_US
dc.identifier.citedreferenceRolls DA, Michailidis G, Hernandez‐Campos F. Queueing analysis of network traffic: methodology and visualization tools. Computer Networks 2005; 48: 447 – 473. DOI: 10.1016/j.comnet.2004.11.016.en_US
dc.identifier.citedreferencePercival DB. Three curious properties of the sample variance and autocovariance for stationary processes with unknown mean. The American Statistician 1993; 47: 274 – 276. DOI: 10.1080/00031305.1993.10475997.en_US
dc.identifier.citedreferenceBrockwell PJ, Davis RA. Time Series: Theory and Methods, Springer Series in Statistics. Springer‐Verlag: New York, 1986.en_US
dc.identifier.citedreferenceChan LK, Heng JC. Skewness correction X ̄ and R charts for skewed distributions. Naval Research Logistics 2003; 50: 555 – 573. DOI: 10.1002/nav.10077.en_US
dc.identifier.citedreferenceYi J, Prybutok VR, Clayton HR. ARL comparisons between neural network models and x ̄ ‐control charts for quality characteristics that are nonnormally distributed. Economic Quality Control 2001; 16: 5 – 15. DOI: 10.1515/EQC.2001.5.en_US
dc.identifier.citedreferenceLowry CA, Woodall WH. A multivariate exponentially weighted moving average control chart. Technometrics 1992; 34: 46 – 53. DOI: 10.2307/1269551.en_US
dc.identifier.citedreferenceDuffield N. Sampling for passive internet measurement: a review. Statistical Science 2004; 19: 472 – 498. DOI: 10.1214/088342304000000206.en_US
dc.identifier.citedreferenceDai JG, Meyn SP. Stability and convergence of moments for multi‐class queueing networks via fluid limit models. IEEE Transactions on Automatic Control 1994; 40: 1889 – 1904. DOI: 10.1109/9.471210.en_US
dc.identifier.citedreferenceWalrand J. Introduction to Queueing Networks. Prentice Hall: Englewood Cliffs, NJ, 1988.en_US
dc.identifier.citedreferenceLeland WE, Taqqu MS, Willinger W, Wilson DV. On the self‐similar nature of Ethernet traffic (extended version). IEEE/ACM Transactions on Networking 1994; 2: 1 – 15. DOI: 10.1109/90.282603.en_US
dc.identifier.citedreferenceNorros I. A storage model with self‐similar input. Queueing Systems: Theory and Applications 1994; 16: 387 – 396. DOI: 10.1007/BF01158964.en_US
dc.identifier.citedreferenceNorros I. On the use of fractional Brownian motion in the theory of connectionless networks. IEEE Journal on Selected Areas in Communications 1995; 13: 953 – 962. DOI: 10.1109/49.400651.en_US
dc.identifier.citedreferenceRoss K, Bambos N. Local search scheduling algorithms for maximal throughput in packet switches. Proceedings of IEEE INFOCOM 2004; 2: 1158 – 1169. DOI: 10.1109/INFCOM.2004.1357002.en_US
dc.identifier.citedreferenceRoss K, Bambos N. Dynamic quality of service control in packet switch scheduling. Proceedings of IEEE International Conference on Communications 2005; 1: 396 – 401. DOI: 10.1109/ICC.2005.1494382.en_US
dc.identifier.citedreferenceBassamboo A, Randhawa RS. On the accuracy of fluid models for capacity sizing in queueing systems with impatient customers. Operations Research 2010; 58: 1398 – 1413. DOI: 10.1287/opre.1100.0815.en_US
dc.identifier.citedreferenceHung YC, Michailidis G. A measurement based dynamic policy for switched processing systems. Proceedings of IEEE International Conference on Communications, Glasgow, Scotland, 2007; 301 – 306, DOI: 10.1109/ICC.2007.57.en_US
dc.identifier.citedreferenceArmony M, Bambos N. Queueing dynamics and maximal throughput scheduling in switched processing systems. Queueing Systems: Theory and Applications 2003; 44: 209 – 252. DOI: 10.1023/A:1024714024248.en_US
dc.identifier.citedreferenceHung YC, Michailidis G. Modeling, scheduling, and simulation of switched processing systems. ACM Transactions on Modeling and Computer Simulation 2008; 18 ( 3 ). DOI: 10.1145/1371574.1371578. Article 12.en_US
dc.identifier.citedreferenceLe Boudec JY. Application of network calculus to guaranteed service networks. IEEE Transactions on Information Theory 1998; 44: 1087 – 1096. DOI: 10.1109/18.669170.en_US
dc.identifier.citedreferenceSorte DD, Reali G. Resource allocation rules for providing performance guarantees to traffic aggregates in a DiffServ environment. Computer Communications 2002; 25: 846 – 862. DOI: 10.1016/S0140‐3664(01)00429‐7.en_US
dc.identifier.citedreferenceAktekin T, Soyer R. Bayesian analysis of queues with impatient customers: applications to call centers. Naval Research Logistics 2012; 59: 441 – 456. DOI: 10.1002/nav.21499.en_US
dc.identifier.citedreferenceBaccelli F, Bremaud P. Elements of Queueing Theory, 2nd edn. Springer: Berlin, Heidelberg, New York, 2003.en_US
dc.identifier.citedreferenceDai JG. On positive harris recurrence of multiclass queueing networks: a unified approach via fluid limit models. Annals of Applied Probability 1995; 5: 49 – 77. DOI: 10.1214/aoap/1177004828.en_US
dc.identifier.citedreferenceDai JG. Stability of Fluid and Stochastic Processing Networks. MaPhySto Miscellanea Publication, No. 9, Aarhus, 1999.en_US
dc.identifier.citedreferenceHarrison JM. The BIGSTEP approach to flow management in stochastic processing networks. In Stochastic Networks: Theory and Applications, Kelly F, Zachary S, Ziendins I (eds). Oxford University Press: Oxford, 1996; 57 – 90.en_US
dc.identifier.citedreferenceHarrison JM. Heavy traffic analysis of a system with parallel servers: asymptotic optimality of discrete‐review policies. Annals of Applied Probability 1998; 8: 822 – 848. DOI: 10.1214/aoap/1028903452.en_US
dc.identifier.citedreferenceMeyn SP, Tweedie RL. Markov Chains and Stochastic Stability. Springer‐Verlag: London, 1993.en_US
dc.identifier.citedreferenceMeyn SP, Tweedie RL. Criteria for stability of Markovian processes III: Foster–Lyapunov criteria for continuous time processes. Advances in Applied Probability 1993; 25: 518 – 548. DOI: 10.2307/1427522.en_US
dc.identifier.citedreferenceStolyar AL. MaxWeight scheduling in a generalized switch: state space collapse and workload minimization in heavy traffic. Annals of Applied Probability 2004; 14: 1 – 53. DOI: 10.1214/aoap/1075828046.en_US
dc.identifier.citedreferenceWasserman KM, Michailidis G, Bambos N. Optimal processor allocation to differentiated job flows. Performance Evaluation 2006; 63: 1 – 14. DOI: 10.1016/j.peva.2004.11.001.en_US
dc.identifier.citedreferenceZeltyn S, Mandelbaum A. Call centers with impatient customers: many‐server asymptotics of the M/M/ n +G queue. Queuing Systems 2005; 51: 36 – 402. DOI: 10.1007/s11134‐005‐3699‐8.en_US
dc.identifier.citedreferenceAktekin T, Soyer R. Call center arrival modeling: a Bayesian state‐space approach. Naval Research Logistics 2011; 58: 28 – 42, DOI: 10.1002/nav.20436.en_US
dc.identifier.citedreferenceWeinberg J, Brown LD, Stroud JR. Bayesian forecasting of an inhomogeneous Poisson process with applications to call center data. Journal of the American Statistical Association 2007; 102: 1185 – 1198. DOI: 10.1198/016214506000001455.en_US
dc.identifier.citedreferenceAsmussen S. Exponential families and regression in the Monte Carlo stud of queues and random walks. Annals of Statistics 1990; 18: 1851 – 1867. DOI: 10.1214/aos/1176347883.en_US
dc.identifier.citedreferenceHung YC, Michailidis G, Bingham DR. Developing efficient simulation methodology for complex queueing networks. Proceedings of the Winter Simulation Conference 2003; 1: 512 – 519. DOI: 10.1109/WSC.2003.1261463.en_US
dc.identifier.citedreferenceWieland JR, Pasupathy R, Schmeiser BW. Queueing‐network stability: simulation‐based checking. Proceedings of the Winter Simulation Conference 2003; 1: 520 – 527. DOI: 10.1109/WSC.2003.1261464.en_US
dc.identifier.citedreferenceKleijnen JPC. Regression metamodels for simulation with common random numbers: Comparison of validation tests and confidence intervals. Management Science 1992; 38: 1164 – 1185. DOI: 10.1287/mnsc.38.8.1164.en_US
dc.identifier.citedreferenceCheng RCH, Kleijnen JPC. Improved design of queueing simulation experiments with highly heteroscedastic responses. Operations Research 1999; 47: 762 – 777. DOI: 10.1287/opre.47.5.762.en_US
dc.identifier.citedreferenceHayel Y, Ouarraou M, Tuffin B. Optimal measurement‐based pricing for an M/M/1 queue. Networks and Spatial Economics 2007; 7: 177 – 195. DOI: 10.1007/s11067‐006‐9001‐8.en_US
dc.identifier.citedreferenceHung YC, Michailidis G. Improving quality of service for switched processing systems, Proceedings of 11th International Workshop on Computer‐Aided Modeling, Analysis and Design of Communication Links and Networks, Trento, Italy, 2006; 46 – 53, DOI: 10.1109/CAMAD.2006.1649717.en_US
dc.identifier.citedreferenceKallitsis MG, Michailidis G, Devetsikiotis M. Measurement‐based optimal resource allocation for network services with pricing differentiation. Performance Evaluation 2009; 66: 505 – 523. DOI: 10.1016/j.peva.2009.03.003.en_US
dc.identifier.citedreferenceXu P, Michailidis G, Devetsikiotis M. Profit‐oriented resource allocation using online scheduling in flexible heterogeneous networks. Telecommunication Systems 2006; 31: 289 – 303. DOI: 10.1007/s11235‐006‐6525‐7.en_US
dc.identifier.citedreferenceEgbelu PJ, Roy N. Material flow control in AGV/unit load based production lines. International Journal of Production Research 1988; 26: 81 – 94. DOI: 10.1080/00207548808947842.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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