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chain At the service side, we can have a similar situation; we then often speak of a multi-mode server General QBD models are very important in studying the performance aspects of communication systems subject to complex arrival streams and to systems with server breakdowns and repairs However, their specification at the level of the block matrices constituting the Markov chain is cumbersome Instead, one should use higher-level mechanisms to construct these models; we will see an example of such an approach in 17 In the QBD models we have addressed in this chapter the state space has been unbounded in one direction In other words, we have addressed system models with an infinite buffer QBD models on a finite state space can be studied with similar means.

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For the MIPHIl and the PHlMll queue, the matrix R that needs to be computed is in fact the same as the one that is computed in the unbounded case; the only difference lies in another normalisation equation For general QBDs on a finite state space, we have to deal with two different boundaries of the state space (one corresponding to the empty system and one corresponding to the completely filled system) This most naturally leads to two second-order matrix equations that need to be solved, as well as two sets of boundary equations The state probabilities gi can then be computed as the sum of two geometric terms A remark should be made here about another powerful solution method for queueing models with a QBD structure known as the spectral expansion method.

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Using this method, the global balance equations for the states in the repeating part of the CTMC are interpreted as a matrix-vector difference equation of second order To solve this difference equation, a characteristic matrix polynomial has to be solved Using an Eigenvalue analysis, the probability vectors for each level can be written as a sum of weighted Eigenvectors; the coefficients in the sum are given by powers of the Eigenvalues Recent comparisons with the logarithmic reduction algorithm show favourable performance for the spectral expansion method in most cases [119] Finally, queues of type G/Ml 1 and MlGll can be handled using matrix-geometric techniques Studying these queues at arrival and departure instances, the embedded Markov chains have an upper and lower triangular form, respectively In practice, this means that we have to deal with matrices Ai (i E JV) in the repeating part of the Markov chain.

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