# Greed Works -- Online Algorithms For Unrelated Machine Stochastic Scheduling

arXiv: https://arxiv.org/abs/1703.01634 · HTML: https://mlsanity.com/paper/1703.01634/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Permutation Predictions for Non-Clairvoyant Scheduling](https://mlsanity.com/paper/2202.10199/) (2022)
- [Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary](https://mlsanity.com/paper/2302.00985/) (2023)
- [Learning-Augmented Online Scheduling with Parsimonious Preemption](https://mlsanity.com/paper/2605.23255/) (2026)

## Abstract (arXiv)

This paper establishes performance guarantees for online algorithms that schedule stochastic, nonpreemptive jobs on unrelated machines to minimize the expected total weighted completion time. Prior work on unrelated machine scheduling with stochastic jobs was restricted to the offline case, and required linear or convex programming relaxations for the assignment of jobs to machines. The algorithms introduced in this paper are purely combinatorial. The performance bounds are of the same order of magnitude as those of earlier work, and depend linearly on an upper bound on the squared coefficient of variation of the jobs' processing times. Specifically for deterministic processing times, without and with release times, the competitive ratios are 4 and 7.216, respectively. As to the technical contribution, the paper shows how dual fitting techniques can be used for stochastic and nonpreemptive scheduling problems.