论文标题
大型平行服务器网络的系统范围内安全人员配备
On system-wide safety staffing of large-scale parallel server networks
论文作者
论文摘要
我们在Halfin-Whitt制度中引入了一个针对任何树拓扑,马尔可夫或非马克维亚的多类多池网络的“系统范围的安全人员配置”(SWSS)参数。当每个服务器池都采用平方根人员配备规则时,该参数可以视为订单$ \ sqrt {n} $的容量波动(正或负)的最佳重新分配。我们提供了SWSS的明确形式作为系统参数的函数,该函数是使用基于高斯消除的图理论方法得出的。 对于马尔可夫网络,我们通过限制扩散的漂移参数对SWSS参数进行等效表征。我们表明,如果SWSS参数为负,则在任何Markov控制下,限制扩散和扩散式排队过程都是瞬态的,并且当此参数为零时,则不能具有固定分布。如果是积极的话,我们表明扩散尺度的排队排队过程是均匀稳定的,也就是说,存在一个调度策略,在该策略下,受控过程的固定分布在网络的大小上紧张。另外,存在一个控制,在该控制下,限制控制的扩散呈指数呈端形。因此,我们已经确定了此类网络在Halfin-Whitt制度中此类网络均匀稳定性的必要条件。 我们使用叶子消除算法产生的恒定控制来稳定限制受控的扩散,而马尔可夫调度策略的一部分易于计算,用于稳定扩散尺度的过程。最后,我们表明,在这些控制下,过程是指数性的,固定分布具有指数式的尾巴。
We introduce a "system-wide safety staffing" (SWSS) parameter for multiclass multi-pool networks of any tree topology, Markovian or non-Markovian, in the Halfin-Whitt regime. This parameter can be regarded as the optimal reallocation of the capacity fluctuations (positive or negative) of order $\sqrt{n}$ when each server pool employs a square-root staffing rule. We provide an explicit form of the SWSS as a function of the system parameters, which is derived using a graph theoretic approach based on Gaussian elimination. For Markovian networks, we give an equivalent characterization of the SWSS parameter via the drift parameters of the limiting diffusion. We show that if the SWSS parameter is negative, the limiting diffusion and the diffusion-scaled queueing processes are transient under any Markov control, and cannot have a stationary distribution when this parameter is zero. If it is positive, we show that the diffusion-scaled queueing processes are uniformly stabilizable, that is, there exists a scheduling policy under which the stationary distributions of the controlled processes are tight over the size of the network. In addition, there exists a control under which the limiting controlled diffusion is exponentially ergodic. Thus we have identified a necessary and sufficient condition for the uniform stabilizability of such networks in the Halfin-Whitt regime. We use a constant control resulting from the leaf elimination algorithm to stabilize the limiting controlled diffusion, while a family of Markov scheduling policies which are easy to compute are used to stabilize the diffusion-scaled processes. Finally, we show that under these controls the processes are exponentially ergodic and the stationary distributions have exponential tails.