Randomized scheduling algorithm for input-queued switches

输入队列交换机的一种随机调度算法(英文)

The sampling problem for input-queued (IQ) randomized scheduling algorithms is analyzed.We observe that if the current scheduling decision is a maximum weighted matching (MWM),the MWM for the next slot mostly falls in those matchings whose weight is closed to the current MWM.Using this heuristic,a novel randomized algorithm for IQ scheduling,named genetic algorithm-like scheduling algorithm (GALSA),is proposed.Evolutionary strategy is used for choosing sampling points in GALSA.GALSA works with only O(N) samples which means that GALSA has lower complexity than the famous randomized scheduling algorithm,APSARA.Simulation results show that the delay performance of GALSA is quite competitive with respect to that of APSARA.

对输入队列随机调度算法的取样问题进行了分析,指出由于输入队列的记忆特性,当前时隙的调度决策若具有最大权值,那么选取与这个最大权值相近的匹配作为下个时隙调度决策时的样点将以较大概率找到最大权值匹配.基于此本文设计了一种新的随机调度算法GALSA,GALSA利用演化策略来跟踪与每个时隙决策具有相近权值的匹配点.GALSA算法所需样点是O(N),因此其复杂性大大低于现有随机算法APSARA.且仿真结果表明GALSA的延迟性能与APSARA媲美.