Virtual Machine Allocation with Lifetime Predictions

Hugo Barbalho · Patricia Kovaleski · Beibin Li · Luke Marshall · Marco Molinaro · Abhisek Pan · Eli Cortez · Matheus Leao · Harsh Patwari · Zuzu Tang · Larissa Rozales Gonçalves · David Dion · Thomas Moscibroda · Ishai Menache

Ballroom B - Position 33
award Outstanding Paper Award
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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.

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