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The ability to properly map services to network resources in a multi-service network is highly important both for making the initial con guration of a network domain, and also as a basis for performing network con guration optimization. The basic task in both cases is the same, although in the latter case there is an initial con guration to start with, as well as potentially a larger number of existing boundary conditions for the new con guration. Resource allocation is an optimization problem where the available network resources, SLAs for different parties, and requirements of service types are examples of boundary conditions. The SLA interfaces relevant to resource allocation are illustrated with an example con guration in Figure 5.1. The responsibility of endto-end service quality can be thought to be by default with service providers, who use SLAs towards network transport providers to implement the service quality. Alternatively, an access network operator may provide a set of services from outsourced Application Service Providers (ASPs), such that the access network operator is responsible for end-to-end service quality. There may be more than one service provider involved, for example, in using IP telephony services in such a way that the

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A ltho ug h in specific cases a network operates at only one frequency (e.g., a powe r syste m network), in general we are interested in the behavior of a network as a functio n of frequency. In a si nusoida l steady-state analysis, the network functio n can be ex pressed as

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The queues of PHIPHI 1 type we have addressed so far are not the only queueing models that can be solved using matrix-geometric methods. In this section we will briefly address other queueing models for which similar methods as those presented here can be applied. First of all, we can use the QBD structure when we are dealing with queues of PHIPHIl type, in a more general context. The QBD structure still exists when the arrival and service process are no longer renewal processes but they are still Markovian. This can best be understood by addressing an arrival process with multiple active modes; in each mode the (Poisson) arrival process may have a different rate. Mode changes in the arrival process can coincide with arrivals, but can also be dependent on another Markov chain, i.e., we deal with a Poisson process of which the rate is modulated by an independent Markov

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of (4.8) in (4.2)-(4.3)

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