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

## TL;DR

DeepPlace is a Deep Reinforcement Learning-based scheduler designed to optimize application placement in multi-tenant clusters by learning resource usage patterns, reducing resource contention, and improving overall cluster utilization.

## Contribution

It introduces a novel Deep RL approach for application placement that adapts to complex resource usage patterns without manual rule creation.

## Key findings

- Reduces resource contention among co-located applications.
- Improves overall cluster utilization.
- Learns effective placement strategies through Deep RL.

## Abstract

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.