An Upper Bound on the M/M/k Queue With Deterministic Setup Times

TL;DR

This paper analyzes the impact of deterministic setup times on multiserver queueing systems, providing the first bounds and approximations for average waiting time, crucial for designing efficient modern systems.

Contribution

It introduces the first closed-form bounds and an approximation for waiting time in multiserver queues with deterministic setup times, using a novel analysis technique called MIST.

Findings

Derived upper and lower bounds on average waiting time

Bounds are within a multiplicative constant of each other

Introduced the Method of Intervening Stopping Times (MIST) technique

Abstract

In many systems, servers do not turn on instantly; instead, a setup time must pass before a server can begin work. These "setup times" can wreak havoc on a system's queueing; this is especially true in modern systems, where servers are regularly turned on and off as a way to reduce operating costs (energy, labor, CO2, etc.). To design modern systems which are both efficient and performant, we need to understand how setup times affect queues. Unfortunately, despite successes in understanding setup in a single-server system, setup in a multiserver system remains poorly understood. To circumvent the main difficulty in analyzing multiserver setup, all existing results assume that setup times are memoryless, i.e. distributed Exponentially. However, in most practical settings, setup times are close to Deterministic, and the widely used Exponential-setup assumption leads to unrealistic model behavior and a dramatic underestimation of the true harm caused by setup times. This paper provides a comprehensive characterization of the average waiting time in a multiserver system with Deterministic setup times, the M/M/k/Setup-Deterministic. In particular, we derive upper and lower bounds on the average waiting time in this system, and show these bounds are within a multiplicative constant of each other. These bounds are the first closed-form characterization of waiting time in any finite-server system with setup times. Further, we demonstrate how to combine our upper and lower bounds to derive a simple and accurate approximation for the average waiting time. These results are all made possible via a new technique for analyzing random time integrals that we named the Method of Intervening Stopping Times, or MIST.