Title: Distributed Rate Scaling in Large-Scale Service Systems Show Chinese title

Title: 大规模服务系统中的分布式速率扩展

Abstract: We consider a large-scale parallel-server system, where each server independently adjusts its processing speed in a decentralized manner. The objective is to minimize the overall cost, which comprises the average cost of maintaining the servers' processing speeds and a non-decreasing function of the tasks' sojourn times. The problem is compounded by the lack of knowledge of the task arrival rate and the absence of a centralized control or communication among the servers. We draw on ideas from stochastic approximation and present a novel rate scaling algorithm that ensures convergence of all server processing speeds to the globally asymptotically optimum value as the system size increases. Apart from the algorithm design, a key contribution of our approach lies in demonstrating how concepts from the stochastic approximation literature can be leveraged to effectively tackle learning problems in large-scale, distributed systems. En route, we also analyze the performance of a fully heterogeneous parallel-server system, where each server has a distinct processing speed, which might be of independent interest. Show Chinese abstract

Abstract: 我们考虑一个大规模的并行服务器系统，其中每个服务器独立地以去中心化的方式调整其处理速度。 目标是使总体成本最小化，该成本包括维持服务器处理速度的平均成本以及任务停留时间的非递减函数。 由于缺乏任务到达率的知识，并且服务器之间没有集中控制或通信，使得问题更加复杂。 我们借鉴了随机逼近的思想，提出了一种新颖的速率缩放算法，确保随着系统规模的增大，所有服务器的处理速度都能收敛到全局渐近最优值。 除了算法设计外，我们方法的一个关键贡献在于展示了如何利用随机逼近文献中的概念来有效解决大规模分布式系统中的学习问题。 在过程中，我们还分析了一个完全异构的并行服务器系统的性能，其中每个服务器具有不同的处理速度，这可能具有独立的兴趣。