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

Jianheng Ling, Pratik Worah, Yawen Wang, Yunchuan Kong, Chunlei Wang, Clifford Stein, Diwakar Gupta, Jason Behmer, Logan Bush, Prakash Ramanan, Rajesh Kumar, Thomas Chestna, Yajing Liu, Ying Liu, Ye Zhao, Kathryn S. McKinley, Meeyoung Park, Martin Maas (anonymized hyperscaler "AnonCorp")

• Why: Hyperscale bin-packing is the canonical capacity-planning problem, and this comes with something rare: fleet-wide production deployment numbers and an honest treatment of what happens when your ML predictions are wrong — the part most papers skip.
• What: VM-to-host allocation using reprediction with lifetime distributions (vs. one-shot point prediction), with algorithms that adapt when mispredictions surface; also novel defragmentation and maintenance handling, which prior academic work ignored. In production at a hyperscaler: ~3% and ~2% reductions in stranded compute and memory, 2.3–9.2 percentage-point increase in empty hosts (helping large-VM availability and system updates), plus fewer migrations. (from abstract only)
• How: Predict full lifetime distributions rather than point estimates; re-predict and re-schedule as evidence accumulates; treat prediction error as a first-class input to the allocation and defrag algorithms rather than an afterthought.
• Where applicable: Org-specific in the sense that gains are calibrated to one hyperscaler's fleet and VM size mix, but the method (distributional prediction + misprediction-adaptive scheduling) is general to any workload-placement problem with lifetimes. The "repredictions beat one-shot predictions" finding is the transferable lesson.