Optimal Choice of Threshold in Two Level Processor Sharing

TL;DR

This paper studies the Two Level Processor Sharing scheduling discipline with hyper-exponential job sizes, deriving formulas for expected sojourn time and optimal thresholds, showing performance benefits with increased job size variance.

Contribution

It provides a closed-form expression for expected sojourn time with two phases and bounds for multiple phases, advancing understanding of threshold optimization in TLPS.

Findings

Expected sojourn time formula for two-phase hyper-exponential distribution

Tight upper bounds for multiple phases

Performance gain increases with job size variance

Abstract

We analyze the Two Level Processor Sharing (TLPS) scheduling discipline with the hyper-exponential job size distribution and with the Poisson arrival process. TLPS is a convenient model to study the benefit of the file size based differentiation in TCP/IP networks. In the case of the hyper-exponential job size distribution with two phases, we find a closed form analytic expression for the expected sojourn time and an approximation for the optimal value of the threshold that minimizes the expected sojourn time. In the case of the hyper-exponential job size distribution with more than two phases, we derive a tight upper bound for the expected sojourn time conditioned on the job size. We show that when the variance of the job size distribution increases, the gain in system performance increases and the sensitivity to the choice of the threshold near its optimal value decreases.