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Index the queue-class pairs by $a=(j,r)$. Let ${\nu }_a$ be the external Poisson process arrival rate, let $p_{ab}$ be the routing probability after class $a$ completes service, and put $p_{a0}=1-{\sum }_b{p}_{ab}$ for the exit probability. An open network has a transient routing matrix $P$, equivalently spectral radius less than one. The effective throughputs solve the traffic equations for a multiclass queueing network:At each queue use the ordered multiclass single-server queue model of part (a), with parameters ${\mu }_{jr},{\gamma }_{ji},{\delta }_{ji}$. An external class-$a$ customer is inserted at position $i$ with rate ${\nu }_a{\delta }_{ji}(N_j+1)$. A customer of class $a$ in position $i$ completes at rate ${\mu }_a{\gamma }_{ji}(N_j)$: it exits with probability $p_{a0}$ or changes to class $b$ and is inserted at its destination with probability $p_{ab}{\delta }_{k\ell }(N_k^{\prime }+1)$. Here $N_k^{\prime }$ is the destination population after the origin deletion, so this also defines routes returning to the same queue. At each node, either service rates are common to its classes or insertion and service fractions form a symmetric service discipline.

Let ${\rho }_j={\sum }_r{\lambda }_{jr}/{\mu }_{jr}<1$. The product-form stationary distribution of a multiclass queueing network isTo prove it by time reversal of a continuous-time Markov chain, interchange each node's insertion and service fractions, and usePairs with ${\lambda }_b=0$ can be deleted. The traffic equations for a multiclass queueing network ensure ${\sum }_a{p}_{ba}^{\ast }+p_{b0}^{\ast }=1$. The ratio of the product weights for an insertion is ${\lambda }_a/{\mu }_a$; for a deletion it is its reciprocal. Consequently every routing, entrance, and exit transition satisfies $\pi (x)q(x,x^{\prime })=\pi (x^{\prime })q^{\ast }(x^{\prime },x)$. The symmetric or common-rate hypothesis makes each node's total service rate identical forward and backward. Summing the traffic equations for a multiclass queueing network also gives ${\sum }_a{\nu }_a^{\ast }={\sum }_a{\nu }_a$. Thus the total transition rates match, proving global balance for a continuous-time Markov chain; each factor is normalized by part (a).

For the class counts the equilibrium law isUnder processor sharing these counts have explicit transition rates ${\nu }_a$ for $n\to n+e_a$, ${\mu }_a{n}_a/N_j{p}_{a0}$ for $n\to n-e_a$, and ${\mu }_a{n}_a/N_j{p}_{ab}$ for $n\to n-e_a+e_b$. Under a closed network, remove external entrances and exits and normalize the same product weights on the conserved customer populations, using a positive solution of the homogeneous traffic equations for a multiclass queueing network. That finite-state normalization does not require the open-network conditions ${\rho }_j<1$.