Published on:
Marios Touloupou, Syed Mafooq Ul Hassan, Jacopo Castellini , Pablo Strasser, Alexandros Kalousis, Herodotos Herodotou
Kubernetes is a popular container orchestration platform in cloud and cloud-edge environments. To properly evaluate new Kubernetes scheduling strategies, especially learningbased 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.
The paper was presented at "The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization - CLOUD COMPUTING 2026" (April 19 to April 23, 2026 - Lisbon, Portugal) during the Special Track "Hyper-CC: Hyper-Distributed Systems and Applications for the Computing Continuum".