Stationary law of a linear flow network ($\pi (n)\propto \left(\genfrac{}{}{0ex}{}{{\sum }_r{n}_r}{n_0}\right){\prod }_r{\rho }_r^{n_r}$)

For the proportionally fair allocation on a linear flow network, independent Poisson flow arrivals at rates ${\nu }_r$ and exponential document sizes of rates ${\mu }_r$ give a continuous-time Markov chain with departures ${\mu }_r{n}_r{x}_r(n)$. Put ${\rho }_r={\nu }_r/{\mu }_r$. Under ${\rho }_0+{\rho }_i<1$ for each local route, the normalizing constant is $Z=(1-{\rho }_0{)}^{I-1}/{\prod }_i(1-{\rho }_0-{\rho }_i)$. The binomial coefficient weight has adjacent-state ratios equal to the aggregate service rates, proving detailed balance. For more general network topologies, proportional fairness need not give this reversible product form.

Summing the stationary law of a linear flow network over its through-flow count gives independent local geometric distributions with ratios $q_i={\rho }_i/(1-{\rho }_0)$. This gives the displayed means. The local counts are independent of one another in this marginal distribution; the through count is generally dependent on them. The independence is a stationary distributional fact, not an assertion that their dynamics evolve independently.