GreenK8s: Green-aware Scheduling for Sustainable Kubernetes Cluster Management

With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in cloud computing environments. However, current schedulers focus solely on performance, lacking awareness of energy types and opportunities t...

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Veröffentlicht in:Proceedings / IEEE International Conference on Cluster Computing S. 1 - 12
Hauptverfasser: Sun, Yifan, Xu, Minxian, Toosi, Adel N.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 02.09.2025
Schlagworte:
Adaptation models
Cloud computing
Cluster architectures
Data centers
Energy efficiency
Energy efficient
Green energy
Green products
Kubernetes
Predictive models
Processor scheduling
Scheduling
Solar energy
Sustainable development
ISSN:2168-9253
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Zusammenfassung:With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in cloud computing environments. However, current schedulers focus solely on performance, lacking awareness of energy types and opportunities to promote green, low-carbon operations. This paper presents a Green-Aware Scheduling Framework for Kubernetes, named GreenK8s, aimed at minimizing the use of brown energy and maximizing the utilization of renewable energy sources, specifically solar power. Our framework integrates real-time power consumption monitoring with predictive solar energy models to intelligently schedule workloads based on energy availability. The proposed solution incorporates an AI-based solar power prediction model, Pod oversubscription strategies, and a novel scheduler, enabling Kubernetes to dynamically adapt to both the type and availability of green energy. Extensive experiments using the real-world Google Borg dataset and a realistic Kubernetes testbed demonstrate that GreenK8s reduces total energy consumption by up to 39 % and increases the average share of green energy in total consumption to 50.65 %, compared to state-of-the-art baselines. This work provides a promising approach to improve operational efficiency and sustainability in data centers.
ISSN:2168-9253
DOI:10.1109/CLUSTER59342.2025.11186459