Zero Queueing for Multi-Server Jobs
Weina Wang, Qiaomin Xie, Mor Harchol-Balter

TL;DR
This paper studies multi-server job queueing systems in large-scale cloud environments, deriving conditions for zero queueing and introducing a novel Lyapunov drift analysis method applicable to complex queueing models.
Contribution
It provides the first stability and queueing probability results for multi-server jobs under large-scale regimes, with a new Lyapunov drift technique.
Findings
Derived conditions for zero queueing in multi-server systems.
Analyzed stability and queueing probabilities in large-scale regimes.
Introduced a novel Lyapunov drift analysis method.
Abstract
Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of complexity to the traditional one-job-per-server model: an arrival might not "fit" into the available servers and might have to queue, blocking later arrivals and leaving servers idle. From a queueing perspective, almost nothing is understood about multi-server job queueing systems; even understanding the exact stability region is a very hard problem. In this paper, we investigate a multi-server job queueing model under scaling regimes where the number of servers in the system grows. Specifically, we consider a system with multiple classes of jobs, where jobs from different classes can request different numbers of servers and have different service time distributions, and jobs…
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Taxonomy
TopicsAdvanced Queuing Theory Analysis · Cloud Computing and Resource Management · Distributed and Parallel Computing Systems
