60 citations · 94 across the 11 of their papers we have counts for
4 papers · 1 filter
Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning
Martin Asenov, Qiwen Deng, Gingfung Yeung +1
Efficiently allocating incoming jobs to nodes in large-scale clusters can lead to substantial improvements in both cluster utilization and job performance. In order to allocate inc…
Performance of Zero-Shot Time Series Foundation Models on Cloud Data
William Toner, Thomas L. Lee, Artjom Joosen +2
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. FMs are trained on numerous diverse datasets and claim to be effectiv…
Lightweight Online Adaption for Time Series Foundation Model Forecasts
Thomas L. Lee, William Toner, Rajkarn Singh +2
Foundation models (FMs) have emerged as a promising approach for time series forecasting. While effective, FMs typically remain fixed during deployment due to the high computationa…
DAM: Towards A Foundation Model for Time Series Forecasting
Luke Darlow, Qiwen Deng, Ahmed Hassan +5
It is challenging to scale time series forecasting models such that they forecast accurately for multiple distinct domains and datasets, all with potentially different underlying c…