Improved queue-size scaling for input-queued switches via graph factorization

This paper studies the scaling of the expected total queue size in an $n\times n$ input-queued switch, as a function of both the load $\rho$ and the system scale $n$. We provide a new class of scheduling policies under which the expected total queue size scales as $O\left( n(1-\rho)^{-4/3} \log \left(\max{\frac{1}{1-\rho}, n}\right)\right)$,overall$n$and$\rho<1$, when the arrival rates are uniform. This improves over the previously best-known scalings in two regimes: $O\left(n^{1.5}(1-\rho)^{-1} \log \frac{1}{1-\rho}\right)$when$\Omega(n^{-1.5}) \le 1-\rho \le O(n^{-1})$and$O\left(\frac{n\log n}{(1-\rho)^2}\right)$when$1-\rho \geq \Omega(n^{-1})$. A key ingredient in our method is a tight characterization of the largest $k$-factor of a random bipartite multigraph, which may be of independent interest.