@inproceedings{barbalho2023virtual,
author = {Barbalho, Hugo and Kovaleski, Patricia and Li, Beibin and Marshall, Luke and Molinaro, Marco and Pan, Abhisek and Cortez, Eli and Leao, Matheus and Patwari, Harsh and Tang, Zuzu and Santos, Tamires and Gonçalves, Larissa Rozales and Dion, David and Moscibroda, Thomas and Menache, Ishai},
title = {Virtual Machine Allocation with Lifetime Predictions},
booktitle = {MLSys},
year = {2023},
month = {June},
abstract = {The emergence of machine learning technology has motivated the use of ML-based predictors in computer systems to improve their efficiency and robustness. However, there are still numerous algorithmic and systems challenges in effectively utilizing ML models in large-scale resource management services that require high throughput and response latency of milliseconds. In this paper, we describe the design and implementation of a VM allocation service that uses ML predictions of the VM lifetime to improve packing efficiencies. We design lifetime-aware placement algorithms that are provably robust to prediction errors and demonstrate their merits in extensive real-trace simulations. We significantly upgraded the VM allocation infrastructure of Microsoft Azure to support such algorithms that require ML inference in the critical path. A robust version of our algorithms has been recently deployed in production and obtains efficiency improvements expected from simulations.},
url = {http://approjects.co.za/?big=en-us/research/publication/virtual-machine-allocation-with-lifetime-predictions/},
}