# Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning

arXiv: https://arxiv.org/abs/2303.13525 · HTML: https://mlsanity.com/paper/2303.13525/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates](https://mlsanity.com/paper/2008.10546/) (2020)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Wasserstein Adversarial Transformer for Cloud Workload Prediction](https://mlsanity.com/paper/2203.06501/) (2022)
- [Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression](https://mlsanity.com/paper/2211.06233/) (2022)

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

- [A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds](https://mlsanity.com/paper/2609.03457/) (2026)

## Abstract (arXiv)

Predicting future resource demand in Cloud Computing is essential for optimizing the trade-off between serving customers' requests efficiently and minimizing the provisioning cost. Modelling prediction uncertainty is also desirable to better inform the resource decision-making process, but research in this field is under-investigated. In this paper, we propose univariate and bivariate Bayesian deep learning models that provide predictions of future workload demand and its uncertainty. We run extensive experiments on Google and Alibaba clusters, where we first train our models with datasets from different cloud providers and compare them with LSTM-based baselines. Results show that modelling the uncertainty of predictions has a positive impact on performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications. Moreover, we investigate whether our models benefit transfer learning capabilities across different domains, i.e. dataset distributions. Experiments on the same workload datasets reveal that acceptable transfer learning performance can be achieved within the same provider (because distributions are more similar). Also, domain knowledge does not transfer when the source and target domains are very different (e.g. from different providers), but this performance degradation can be mitigated by increasing the training set size of the source domain.