Load balancing and auto scaling are at the core of scalable, contemporary systems, addressing dynamic resource allocation and service rate adjustments in response to workload changes. This paper introduces a novel model and algorithms for tuning load balancers coupled with auto scalers, considering bursty traffic arriving at finite queues. We begin by presenting the problem as a weakly coupled Markov Decision Processes (MDP), solvable via a linear program (LP). However, as the number of control variables of such LP grows combinatorially, we introduce a more tractable relaxed LP formulation, and extend it to tackle the problem of online parameter learning and policy optimization using a two-timescale algorithm based on the LP Lagrangian.

翻译：负载均衡与自动扩缩是可扩展现代系统的核心，旨在根据工作负载变化实现动态资源分配与服务速率调整。本文针对到达有限队列的突发流量场景，提出了一种用于协调负载均衡器与自动扩缩器的新型模型与算法。我们首先将该问题建模为可通过线性规划求解的弱耦合马尔可夫决策过程。然而，由于此类线性规划的控制变量数量呈组合增长，我们引入了更易处理的松弛线性规划形式，并进一步扩展该方法，利用基于线性规划拉格朗日函数的双时间尺度算法，实现在线参数学习与策略优化。