Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head Of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomized algorithms have been proposed in the literature. But they commonly perform poorly when input traffic does not uniformly select output ports. In this paper, we propose two randomized algorithms that outperform the wellknown formerly proposed solutions. We exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms.

<span>Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head Of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomized algorithms have been proposed in the literature. But they commonly perform poorly when input traffic does not uniformly select output ports. In this paper, we propose two randomized algorithms that outperform the well-known formerly proposed solutions. We exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms.</span>