A New Discrete-Time Multi-Constrained -Winner-Take-All Recurrent Network and Its Application to Prioritized Scheduling

摘要

In this paper, we propose a novel discrete-time recurrent neural network aiming to resolve a new class of multi-constrained -winner-take-all ( -WTA) problems. By facilitating specially designed asymmetric neuron weights, the proposed model is capable of operating in a fully parallel manner, thereby allowing true digital implementation. This paper also provides theorems that delineate the theoretical upper bound of the convergence latency, which is merely . Importantly, via simulations, the average convergence time is close to in most general cases. Moreover, as the multi-constrained -WTA problem degenerates to a traditional single-constrained problem, the upper bound becomes exactly two parallel iterations, which significantly outperforms the existing -WTA models. By associating the neurons and neuron weights with routing paths and path priorities, respectively, we then apply the model to a prioritized flow scheduler for the data center networks. Through extensive simulations, we demonstrate that the proposed scheduler converges to the equilibrium state within near-constant time for different scales of networks while achieving maximal throughput, quality-of-service priority differentiation, and minimum energy consumption, subject to the flow contention-free constraints.