[2508.12773] Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status
[2508.12773] Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling --> Computer Science > Machine Learning arXiv:2508.12773 (cs) [Submitted on 18 Aug 2025] Title: Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling Authors: Jiadong Chen , Xiao He , Hengyu Ye , Fuxin Jiang , Tieying Zhang , Jianjun Chen , Xiaofeng Gao View a PDF of the paper titled Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling, by Jiadong Chen and 6 other authors View PDF HTML (experimental) Abstr
Explore this link on the map →saved by
related reading
- How To Scale Your Modeljax-ml.github.io
- Trending Papers - Hugging Facepaperswithcode.com
- LLM Engineer's Almanac - Workloads | Modalmodal.com
- Real-time machine learning: challenges and solutionshuyenchip.com
- Clouded Judgement 7.10.26 - Own Your Weightscloudedjudgement.substack.com
- My picture of the present in AI — LessWronglesswrong.com
- trees are harlequins, words are harlequins - I don't think you're drawing the right lesson from...nostalgebraist.tumblr.com
- Forecasting transformative AI: the "biological anchors" method in a nutshellcold-takes.com
- Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Mostarxiv.org
- Components of an Open Source AI Compute Tech Stackanyscale.com
- [2207.00032] DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scalearxiv.org
- Efficiently Scaling Transformer Inferencearxiv.org