An RL-Based Model for Optimized Kubernetes Scheduling

authored by

John Rothman, Javad Chamanara

Abstract

In this paper, we present RLKube, a Reinforcement Learning (RL)-based custom Kubernetes (K8s) scheduler plugin for optimized task scheduling. RLKube objectives are maximizing resource utilization and Pod throughput as well as improving energy efficiency in a K8s cluster. We used Double Deep Q-Network (DDQN) with Prioritized Experience Replay (PER) and utilized different reward functions to train the RL agent. Also, we have developed corresponding policies for each objective. We have evaluated the effectiveness of RLKube using various datasets simulating a diverse set of realistic load patterns. The results show that RLKube outperforms the default K8s scheduling policies in terms of throughput and energy usage, highlighting its potential for Improving task scheduling in K8s clusters.

Organisation(s)

L3S Research Centre

Type

Conference contribution

No. of pages

6

Publication date

2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Networks and Communications, Software

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.1109/ICNP59255.2023.10355623 (Access: Closed)