Economies-of-Scale in Many-Server Queueing Systems: Tutorial and Partial Review of the QED Halfin-Whitt Heavy-Traffic Regime
Economies-of-Scale in Many-Server Queueing Systems: Tutorial and Partial Review of the QED Halfin-Whitt Heavy-Traffic Regime
复制标题
多服务器排队系统中的规模经济：QED Halfin-Whitt 大流量制度的教程和部分回顾
DOI：
10.1137/17m1133944
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
B. Zwart
中科院分区：
文献类型：
--
作者：
J. V. Leeuwaarden;B. Mathijsen;B. Zwart
多服务器排队系统描述了用户需要来自多个并行服务器的服务的情况。例如，机场的值机队伍、医院的候诊室、联系中心的排队、无线网络中的数据缓冲区以及云数据中心的延迟服务。这些都是具有大容量级别的作业(客户端、患者、任务)和服务器(代理、床、处理器)的情况，从几十个数量级(结账)到数千个数量级(处理器)。这项调查调查了如何设计这样的系统，以利用资源汇集和规模经济。特别是，我们回顾了质量和效率驱动(QED)制度背后的数学原理，该制度使系统在接近充分利用的情况下运行，同时服务器数量大量增长，延迟仍然可控。面向广大读者，我们详细描述了基本的马尔可夫多服务器系统的数学概念，我们只提供与负载平衡、过度分散、参数不确定性、一般服务要求和排队网络等相关的更高级设置的草图或参考。虽然作为对大量工作的部分调查，本教程并不意味着详尽无遗。
Multiserver queueing systems describe situations in which users require service from multiple parallel servers. Examples include check-in lines at airports, waiting rooms in hospitals, queues in contact centers, data buffers in wireless networks, and delayed service in cloud data centers. These are all situations with jobs (clients, patients, tasks) and servers (agents, beds, processors) that have large capacity levels, ranging from the order of tens (checkouts) to thousands (processors). This survey investigates how to design such systems to exploit resource pooling and economies-of-scale. In particular, we review the mathematics behind the quality- and efficiency-driven (QED) regime, which lets the system operate close to full utilization, while the number of servers grows simultaneously large and delays remain manageable. Aimed at a broad audience, we describe in detail the mathematical concepts for the basic Markovian many-server system, and we provide only sketches or references for more advanced settings related to, e.g., load balancing, overdispersion, parameter uncertainty, general service requirements, and queueing networks. While serving as a partial survey of a massive body of work, the tutorial is not meant to be exhaustive.
超越大流量制度：单服务器队列的通用边界和控制
影响因子：
2.7
作者：
Huang, Junfei;Gurvich, Itai
通讯作者：
Gurvich, Itai
随着到达时间和客户反馈的变化，稳定服务系统的性能
影响因子：
6.4
作者：
Liu, Yunan;Whitt, Ward
通讯作者：
Whitt, Ward