# LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions

arXiv: https://arxiv.org/abs/2412.09840 · HTML: https://mlsanity.com/paper/2412.09840/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [Astrolabe: Balancing Load in LLM Serving with Randomized Prediction-Guided Scheduling](https://mlsanity.com/paper/2508.03611/) (2025)
- [AI for Distributed Systems Design: Scalable Cloud Optimization Through Repeated LLMs Sampling And Simulators](https://mlsanity.com/paper/2510.18897/) (2025)
- [Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search](https://mlsanity.com/paper/2512.25065/) (2025)
- [TuxBot: Semantic-Aware Online OS Tuning with Large Language Models](https://mlsanity.com/paper/2605.15026/) (2026)

## Abstract (arXiv)

Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and adjusts VM and host lifetimes when incorrect predictions emerge. We also present novel approaches for defragmentation and regular system maintenance, which are essential to our data center reliability and optimizations, and are not explored in prior work. We show repredictions deliver a fundamental advance in effectiveness over one-shot prediction. We call our novel combination of distribution-based lifetime predictions and scheduling algorithms Lifetime Aware VM Allocation (LAVA). LAVA reduces resource stranding and increases the number of empty hosts, which are critical for large VM scheduling, cloud system updates, and reducing dynamic energy consumption. Our approach runs in production within Google's hyperscale cloud data centers, where it improves efficiency by decreasing stranded compute and memory resources by ~3% and ~2% respectively. It increases empty hosts by 2.3-9.2 pp in production, reducing dynamic energy consumption, and increasing availability for large VMs and cloud system updates. We also show a reduction in VM migrations for host defragmentation and maintenance. In addition to our fleet-wide production deployment, we perform simulation studies to characterize the design space and show that our algorithm significantly outperforms the prior state of the art lifetime-based scheduling approach.