On the stochastic and asymptotic improvement of First-Come First-Served and Nudge scheduling
Benny Van Houdt

TL;DR
This paper introduces and analyzes Nudge-K, a scheduling algorithm that improves upon FCFS by allowing limited swaps between two job types, with explicit response time distributions and asymptotic tail improvements.
Contribution
It extends the Nudge scheduling concept to a two-job-type system with limited swapping, providing explicit response time distributions and asymptotic tail improvement analysis.
Findings
Nudge-K improves response times over FCFS under certain conditions.
The asymptotic tail improvement ratio (ATIR) is positive and maximized at large K.
Optimal K tends to infinity in heavy traffic when type-2 jobs are longer.
Abstract
Recently it was shown that, contrary to expectations, the First-Come-First-Served (FCFS) scheduling algorithm can be stochastically improved upon by a scheduling algorithm called {\it Nudge} for light-tailed job size distributions. Nudge partitions jobs into 4 types based on their size, say small, medium, large and huge jobs. Nudge operates identical to FCFS, except that whenever a {\it small} job arrives that finds a {\it large} job waiting at the back of the queue, Nudge swaps the small job with the large one unless the large job was already involved in an earlier swap. In this paper, we show that FCFS can be stochastically improved upon under far weaker conditions. We consider a system with job types and limited swapping between type- and type- jobs, but where a type- job is not necessarily smaller than a type- job. More specifically, we introduce and study the…
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Taxonomy
TopicsAdvanced Queuing Theory Analysis · Network Traffic and Congestion Control · Advanced Optical Network Technologies
