Minimizing $\ell_2$ Norm of Flow Time by Starvation Mitigation

TL;DR

This paper proposes a new scheduling approach that combines FCFS and SRPT to better balance average and maximum flow times, reducing starvation and improving competitive ratios.

Contribution

It introduces a starvation mitigation method using FCFS to enhance SRPT, achieving a better competitive ratio of O(n^{1/3}) for minimizing the $ ext{ell}_2$ norm of flow time.

Findings

Mitigating starvation improves scheduling performance.

The proposed method achieves an O(n^{1/3}) competitive ratio.

First theoretical evidence linking starvation mitigation to performance gains.

Abstract

The assessment of a job's Quality of Service (QoS) often revolves around its flow time, also referred to as response time. This study delves into two fundamental objectives for scheduling jobs: the average flow time and the maximum flow time. While the Shortest Remaining Processing Time (SRPT) algorithm minimizes average flow time, it can result in job starvation, causing certain jobs to experience disproportionately long and unfair flow times. In contrast, the First-Come-First-Served (FCFS) algorithm minimizes the maximum flow time but may compromise the average flow time. To strike a balance between these two objectives, a common approach is to minimize the $\ell_2$ norm of flow time. SRPT and FCFS are $O(n^{\frac{1}{2}})$-competitive for this problem, where $n$ is the number of jobs. Prior to this work, no algorithm is known to achieve a competitive ratio better than SRPT and FCFS. In this paper, we use FCFS to mitigate the starvation caused by SRPT. Given a good estimate of $n$, we prove that this approach achieves a much better competitive ratio of $O(n^{\frac{1}{3}})$. Our results provide the first theoretical evidence that mitigating starvation in SRPT leads to a provable improvement in scheduling performance.