Invariant States of Hydrodynamic Limits of Randomized Load-Balancing Networks
研究了随机化负载均衡网络中流体力学方程的不变状态，证明了在适当条件下队列长度分布具有双指数尾部衰减，并提供了数值证据支持不变状态是N服务器模型稳态分布极限的猜想。
Randomized load-balancing algorithms play an important role in improving performance in large-scale networks at relatively low computational cost. A common model of such a system is a network of N parallel queues in which incoming jobs with independent and identically distributed service times are routed on arrival using the join-the-shortest-of-d-queues routing algorithm. Under fairly general conditions, it was shown by Aghajani and Ramanan that as [Formula: see text], the state dynamics converge to the unique solution of a countable system of coupled deterministic measure-valued equations called the hydrodynamic equations. In this article, a characterization of invariant states of these hydrodynamic equations is obtained and, when [Formula: see text], used to construct a numerical algorithm to compute the queue length distribution and mean virtual waiting time in the invariant state. Additionally, it is also shown that under a suitable tail condition on the service distribution, the queue length distribution of the invariant state exhibits a doubly exponential tail decay, thus demonstrating a vast improvement in performance over the case [Formula: see text], which corresponds to random routing, when the tail decay could even be polynomial. Furthermore, numerical evidence is provided to support the conjecture that the invariant state is the limit of the steady-state distributions of the N-server models. The proof methodology, which entails analysis of a coupled system of measure-valued equations, can potentially be applied to other many-server systems with general service distributions, where measure-valued representations are useful. Funding: This work was supported by the Office of Naval Research Vannevar Bush Faculty Fellowship [Grant ONR-N0014-21-1-2887], the Division of Mathematical Sciences [Grants DMS-1407504, DMS-1713032, and DMS-2246838], and the Army Research Office [Grant W911NF2010133].