Copyright © 2005 Department of Computer Science CPSC 641 Winter 20111 Markov Chains Plan: –Introduce basics of Markov models –Define terminology for Markov.

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Discrete time Markov Chain

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Presentation transcript:

Copyright © 2005 Department of Computer Science CPSC 641 Winter Markov Chains Plan: –Introduce basics of Markov models –Define terminology for Markov chains –Discuss properties of Markov chains –Show examples of Markov chain analysis On-Off traffic model Markov-Modulated Poisson Process Erlang B blocking formula TCP congestion window evolution

Copyright © 2005 Department of Computer Science CPSC 641 Winter Definition: Markov Chain A discrete-state Markov process Has a set S of discrete states: |S| > 1 Changes randomly between states in a sequence of discrete steps Continuous-time process, although the states are discrete Very general modeling technique used for system state, occupancy, traffic, queues,... Analogy: Finite State Machine (FSM) in CS

Copyright © 2005 Department of Computer Science CPSC 641 Winter Some Terminology (1 of 3) Markov property: behaviour of a Markov process depends only on what state it is in, and not on its past history (i.e., how it got there, or when) A manifestation of the memoryless property, from the underlying assumption of exponential distributions

Copyright © 2005 Department of Computer Science CPSC 641 Winter Some Terminology (2 of 3) The time spent in a given state on a given visit is called the sojourn time Sojourn times are exponentially distributed and independent Each state i has a parameter q_i that characterizes its sojourn behaviour

Copyright © 2005 Department of Computer Science CPSC 641 Winter Some Terminology (3 of 3) The probability of changing from state i to state j is denoted by p_ij This is called the transition probability (sometimes called transition rate) Often expressed in matrix format Important parameters that characterize the system behaviour

Copyright © 2005 Department of Computer Science CPSC 641 Winter Properties of Markov Chains Irreducibility: every state is reachable from every other state (i.e., there are no useless, redundant, or dead-end states) Ergodicity: a Markov chain is ergodic if it is irreducible, aperiodic, and positive recurrent (i.e., can eventually return to a given state within finite time, and there are different path lengths for doing so) Stationarity: stable behaviour over time

Copyright © 2005 Department of Computer Science CPSC 641 Winter Analysis of Markov Chains The analysis of Markov chains focuses on steady-state behaviour of the system Called equilibrium, or long-run behaviour as time t approaches infinity Well-defined state probabilities p_i (non- negative, normalized, exclusive) Flow balance equations can be applied

Copyright © 2005 Department of Computer Science CPSC 641 Winter Examples of Markov Chains Traffic modeling: On-Off process Interrupted Poisson Process (IPP) Markov-Modulated Poisson Process Computer repair models (server farm) Erlang B blocking formula Birth-Death processes M/M/1 Queueing Analysis