An RL-Based Model for Optimized Kubernetes Scheduling

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Typ

Aufsatz in Konferenzband

Anzahl der Seiten

6

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computernetzwerke und -kommunikation, Software

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1109/ICNP59255.2023.10355623 (Zugang: Geschlossen)