Home // CLOUD COMPUTING 2026, The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization // View article

A Modular Kubernetes Workload Simulator for Evaluating Learning-Based Scheduling Policies

Authors:
Marios Touloupou
Syed Mafooq Ul Hassan
Jacopo Castellini
Pablo Strasser
Alexandros Kalousis
Herodotos Herodotou

Keywords: Kubernetes; workload simulation; workload scheduling; experimentation.

Abstract:
Kubernetes is a popular container orchestration platform in cloud and cloud-edge environments. To properly evaluate new Kubernetes scheduling strategies, especially learning-based ones, a controlled but realistic environment is needed, enabling repeatable experimentation without the overhead of running real clusters. This paper presents a modular Kubernetes workload simulator that supports configurable cluster setups, synthetic workload generation, and pluggable scheduling policies. The simulator enables both rule-based and learning-based schedulers to be evaluated under identical conditions, while providing detailed execution traces and performance metrics. This demo paper showcases its capabilities through some interactive scenarios that highlight its usefulness for the development, testing, and comparative evaluation of different Kubernetes schedulers.

Pages: 1 to 2

Copyright: Copyright (c) IARIA, 2026

Publication date: April 19, 2026

Published in: conference

ISSN: 2308-4294

ISBN: 978-1-68558-373-6

Location: Lisbon, Portugal

Dates: from April 19, 2026 to April 23, 2026