Erlang loss formula ($E(a,C)$)

For capacity $C$ and offered traffic $a$, the probability of a full single-resource loss system is $E(a,C)=(a^C/C!)/({\sum }_{k=0}^C{a}^k/k!)$. The stable recursion is $E(a,0)=1$, $E(a,k)=aE(a,k-1)/(k+aE(a,k-1))$.

For rational offered load, every step of the stable recursion for the Erlang loss formula is rational. Comparing the final numerator and denominator with rational thresholds therefore certifies signs without floating-point uncertainty. Opposite certified signs of a continuous fixed-point residual imply a root by the intermediate value theorem; several alternating intervals can prove multiple Erlang fixed points under alternative routing.

For positive integer capacity $C$ and offered load $a>0$, let $m_C(a)$ be the carried load of an Erlang loss resource. Differentiating the upper-truncated Poisson distribution gives $m_C^{\prime }(a)=\mathrm{Var}(N)/a>0$. Differentiating its probability at $C$ gives the displayed derivative of the Erlang loss formula; it is positive because $m_C(a)<C$. Thus blocking increases continuously from zero to one, and the carried load increases from zero to $C$. These properties justify the inverse blocking coordinates in the convex potential for the Erlang fixed point.

Scale the offered load and capacity together. Reversing the denominator of the Erlang loss formula givesFor each fixed $j$, the summand tends to $(C/\nu {)}^j$. If $\nu >C$, the dominated convergence theorem applies with the summable geometric series bound $(C/\nu {)}^j$. Otherwise each fixed finite partial sum has limiting value at least its number of terms, so the reciprocal diverges. Taking reciprocals proves the formula, including the critical case $\nu =C$.

For offered load $a$ and capacity $C\ge 1$, the carried load equals the mean occupancy:Differentiating the truncated-Poisson equilibrium weights gives $m_C^{\prime }(a)={\mathrm{Var}}_a(N)/a>0$ for $a>0$. The carried load increases from zero to $C$. This monotonicity supplies the strictly increasing term in the convex potential for the Erlang fixed point.

The Erlang fixed point approximation treats resource blocking as independent and applies the Erlang loss formula to traffic screened by other resources. Its self-consistency equations have a unique fixed point under fixed routing, but can have several under alternative routing.

Set $y_j=-\log (1-B_j)$ and let $e_C(a)=E(a,C)$. For positive integer capacities,The function $U$ is the carried load of an Erlang loss resource evaluated at its inverse blocking parametrization. It is continuous and strictly increasing from zero to $C$. Hence $F$ is a coercive function and a strictly convex function on the nonnegative orthant. Its first-order conditions are the fixed-routing Erlang fixed point equations, including zero prices at unused links.

Update one link blocking probability at a time using the reduced load accepted by all other links, then cycle through the links. In logarithmic acceptance coordinates this is exact coordinate descent for the convex potential for the Erlang fixed point. Potential values decrease within a compact sublevel set. Continuity of a whole-sweep update forces every limit point to minimize every coordinate, hence to equal the unique global minimizer. This proves convergence without requiring a simultaneous substitution scheme.

For unit resource requirements in a loss network, the effective offered traffic to resource $j$ is $a_j={\sum }_{r:j\in r}{\alpha }_r{\prod }_{k\in r\setminus \{j\}}(1-B_k)$, where $B_k$ are approximate resource blocking probabilities. The resource being modeled is excluded from its own screening product.