# DeepPlace: Learning to Place Applications in Multi-Tenant Clusters

arXiv: https://arxiv.org/abs/1907.12916 · HTML: https://mlsanity.com/paper/1907.12916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Network Contention-Aware Cluster Scheduling with Reinforcement Learning](https://mlsanity.com/paper/2310.20209/) (2023)

## Abstract (arXiv)

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present DeepPlace, a scheduler that learns to exploits various temporal resource usage patterns of applications using Deep Reinforcement Learning (Deep RL) to reduce resource competition across jobs running in the same machine while at the same time optimizing for overall cluster utilization.