# Truthful Online Scheduling of Cloud Workloads under Uncertainty

arXiv: https://arxiv.org/abs/2203.01213 · HTML: https://mlsanity.com/paper/2203.01213/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2022.

## Built on by (most cited first, 1 of 1)

- [Why does Prediction Accuracy Decrease over Time? Uncertain Positive Learning for Cloud Failure Prediction](https://mlsanity.com/paper/2402.00034/) (2024)

## Abstract (arXiv)

Cloud computing customers often submit repeating jobs and computation pipelines on approximately regular schedules, with arrival and running times that exhibit variance. This pattern, typical of training tasks in machine learning, allows customers to partially predict future job requirements. We develop a model of cloud computing platforms that receive statements of work (SoWs) in an online fashion. The SoWs describe future jobs whose arrival times and durations are probabilistic, and whose utility to the submitting agents declines with completion time. The arrival and duration distributions, as well as the utility functions, are considered private customer information and are reported by strategic agents to a scheduler that is optimizing for social welfare. We design pricing, scheduling, and eviction mechanisms that incentivize truthful reporting of SoWs. An important challenge is maintaining incentives despite the possibility of the platform becoming saturated. We introduce a framework to reduce scheduling under uncertainty to a relaxed scheduling problem without uncertainty. Using this framework, we tackle both adversarial and stochastic submissions of statements of work, and obtain logarithmic and constant competitive mechanisms, respectively.