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Inferring Queueing Network Models from High-Precision Location Tracking Data

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Inferring Queueing Network Models from High-Precision Location Tracking Data University of London Imperial College of Science, Technology and Medicine Department of Computing Inferring Queueing Network Models from High-precision Location Tracking Data Tzu-Ching Horng Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Computing of the University of London and the Diploma of Imperial College, May 2013 Abstract Stochastic performance models are widely used to analyse the performance and reliability of systems that involve the flow and processing of customers. However, traditional methods of constructing a performance model are typically manual, time-consuming, intrusive and labour- intensive. The limited amount and low quality of manually-collected data often lead to an inaccurate picture of customer flows and poor estimates of model parameters. Driven by ad- vances in wireless sensor technologies, recent real-time location systems (RTLSs) enable the automatic, continuous and unintrusive collection of high-precision location tracking data, in both indoor and outdoor environment. This high-quality data provides an ideal basis for the construction of high-fidelity performance models. This thesis presents a four-stage data processing pipeline which takes as input high-precision location tracking data and automatically constructs a queueing network performance model approximating the underlying system. The first two stages transform raw location traces into high-level “event logs” recording when and for how long a customer entity requests service from a server entity. The third stage infers the customer flow structure and extracts samples of time delays involved in the system; including service time, customer interarrival time and customer travelling time. The fourth stage parameterises the service process and customer arrival process of the final output queueing network model. To collect large-enough location traces for the purpose of inference by conducting physical ex- periments is expensive, labour-intensive and time-consuming. We thus developed LocTrack- JINQS, an open-source simulation library for constructing simulations with location awareness and generating synthetic location tracking data. Finally we examine the effectiveness of the data processing pipeline through four case studies based on both synthetic and real location tracking data. The results show that the methodology performs with moderate success in inferring multi-class queueing networks composed of single- server queues with FIFO, LIFO and priority-based service disciplines; it is also capable of inferring different routing policies, including simple probabilistic routing, class-based routing and shortest-queue routing. i ii Acknowledgements The completion of this research is largely owe to many of those I have worked with during my study at Imperial College London. I especially would like to express my gratitude to my supervisor Dr. William Knottenbelt, for his generosity, open-mindedness, enthusiasm as well as constant support and guidance during my PhD; and to my colleague Nikolas Anastasiou, for all the work we have done together and our personal friendship. The PhD journey for me has been a long one with countless moments of self-questioning, doubts and confusion. Fortunately, there are many beloved ones always taking my side along the journey. Special thanks to my dearest friends – Pei-Chien Tsai, Tzu-Chun Chen, Cecilia Lopez, Olivier Blesbois – for always being there for me. Last, but most important of all, I owe my parents the deepest gratitude for everything they have given me throughout my life, with their unconditional love. iii iv Dedication This thesis is dedicated to Tzu-Ting Horng, who understands me the most and always has a place in my heart. v vi Contents Abstract i Acknowledgements iii 1 Introduction 1 1.1 Motivation....................................... 1 1.2 Objectives....................................... 7 1.3 Contributions ..................................... 8 1.3.1 Inferring Queueing Network Models from High-precision Location Track- ingData.................................... 8 1.3.2 LocTrackJIQNS................................ 9 1.4 ThesisOutline..................................... 10 1.5 Statement of Originality and Publications . .. 11 2 Background 13 2.1 StochasticProcesses ............................... .. 13 2.1.1 RenewalProcesses .............................. 14 vii viii CONTENTS 2.1.2 PoissonProcesses............................... 15 2.1.3 MarkovChainandMarkovProcesses . 17 2.2 QueueingTheoryandSimpleQueues . 22 2.2.1 Queueingtheory ............................... 23 2.2.2 General birth-and-death process and M/M/1queue............ 27 2.3 QueueingNetwork .................................. 27 2.3.1 JacksonQueueingNetwork. 29 2.3.2 GordonandNewellnetworks. 31 2.3.3 BCMPqueueingnetwork. .. .. 32 2.4 Distribution fitting . 33 2.4.1 Maximum Likelihood Method . 33 2.4.2 EMAlgorithm................................. 34 2.4.3 Statisticalgoodness-of-fittests. .... 36 2.4.4 Kolmogorov-Smirnovtest . .. .. 36 2.4.5 Anderson-Darlingtest ............................ 37 2.4.6 Chi-squaretest ................................ 38 2.4.7 ModelSelectionUsingAIC. .. .. 39 2.5 Phase-type Distributions and their applications in traffic modelling . ...... 40 2.5.1 Overview of phase-type distributions . 42 2.5.2 Fitting traffic measurements with phase-type distributions . ..... 43 2.6 Wireless Real-time Location Systems . .. 47 CONTENTS ix 2.6.1 Localisation Techniques . 47 2.6.2 Wireless Positioning Technologies and Systems . 50 2.6.3 Ultra-wideband(UWB) ........................... 55 2.6.4 WirelessLocalAreaNetwork(WLAN) . 56 2.6.5 Location-aware applications . 57 2.7 OtherRelatedWork ................................. 58 2.7.1 WorkflowMining ............................... 58 2.7.2 Constructing Queueing Models for Performance Analysis of Real-life Sys- tems...................................... 60 3 Inferring Queueing Networks from High-precision Location Tracking Data 64 3.1 Introduction...................................... 64 3.2 Stage1......................................... 65 3.2.1 Output..................................... 67 3.3 Stage2......................................... 68 3.3.1 Inferring locations of service centres . ... 68 3.3.2 Generationofeventtable........................... 74 3.3.3 Output..................................... 76 3.4 Stage3......................................... 78 3.4.1 Customer flow structure mining and extraction of travelling time and interarrivaltimesamples . .. .. 78 3.4.2 Extracting service time traces and inferring service disciplines . ..... 81 x CONTENTS 3.4.3 Output..................................... 85 3.5 Stage4......................................... 85 3.5.1 Performance traces distribution fitting . .. 85 3.5.2 Output..................................... 87 3.6 Summary ....................................... 88 4 LocTrackJINQS: A Location-aware Simulation Tool for Multiclass QN Mod- els 90 4.1 Introduction...................................... 90 4.2 JINQS and Major Extensions in LocTrackJINQS . .... 94 4.3 Implementation .................................... 95 4.3.1 QueueingNetworkStructure. 95 4.3.2 Generating Synthetic Location Tracking Data . .. 99 4.3.3 New Event classes .............................. 99 4.3.4 GraphicUserInterface ............................100 4.4 UserInputsandSimulationOutputs . 100 4.4.1 UserInputs ..................................100 4.4.2 SimulationOutputs..............................103 4.5 SoftwareDemonstration . .. .. .. 104 4.6 Summary .......................................106 5 Case Studies 108 5.1 Case Study 1: Simple Queueing Network . 111 CONTENTS xi 5.1.1 Systemsettings ................................111 5.1.2 Distribution fitting results . 112 5.1.3 Inferred queueing network model and response time analysis . ......113 5.2 Case Study 2: Multiclass Queueing Network with Different Service Disciplines . 116 5.2.1 Systemsettings ................................116 5.2.2 Distribution fitting results . 117 5.2.3 Inferred queueing network model and response time analysis . ......120 5.3 Case Study 3: Queueing Network with Different Routing Policies . ......125 5.3.1 Systemsettings ................................125 5.3.2 Distribution fitting results . 126 5.3.3 Inferred queueing network model and response time analysis . ......128 5.4 CaseStudy4:ExperimentData. 133 5.4.1 Experiment design and settings . 133 5.4.2 Results.....................................134 5.5 Discussion.......................................136 5.5.1 Time delay sample extraction and distribution fitting . 136 5.5.2 Customerflowrouting ............................139 5.5.3 Response time analysis . 140 5.5.4 ComputationTime ..............................140 5.6 Summary .......................................141 6 Conclusion 143 6.1 Summary of Thesis Achievements . 143 6.1.1 Develop a methodology for inferring a queueing network model from high- precisionlocationtrackingdata . 143 6.1.2 Implementation of LocTrackJINQS ...................144 6.1.3 Evaluate the accuracy of the developed data processing pipeline .....144 6.2 Applications......................................145 6.3 Futurework......................................146 6.3.1 Extend the variety of features that can be inferred . ......146 6.3.2 Extensions on LocTrackJINQS ......................147 6.3.3 Online data processing pipeline . 147 6.3.4 Investigate different filtering and smoothing techniques for location track- ingdata ....................................149 6.3.5 Support
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