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.

该研究介绍了一种针对具有突发流量的有限队列的负载均衡和自动缩放的新模型和算法，通过线性规划解决了弱耦合的马尔可夫决策过程问题，进一步将其放松为可操作的松弛线性规划，并基于线性规划的拉格朗日函数提出了一个两时间尺度算法来解决在线参数学习和策略优化的问题。