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arXiv · 2506.04902

Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS

Preethika Pradeep·Eyhab Al-Masri
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Abstract

AIoT workloads demand energy-efficient orchestration across cloud-edge infrastructures, but Kubernetes' default scheduler lacks multi-criteria optimization for heterogeneous environments. This paper presents GreenPod, a TOPSIS-based scheduler optimizing pod placement based on execution time, energy consumption, processing core, memory availability, and resource balance. Tested on a heterogeneous Google Kubernetes cluster, GreenPod improves energy efficiency by up to 39.1% over the default Kubernetes (K8s) scheduler, particularly with energy-centric weighting schemes. Medium complexity workloads showed the highest energy savings, despite slight scheduling latency. GreenPod effectively balances sustainability and performance for AIoT applications.

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BibTeXRIS

Preethika Pradeep, Eyhab Al-Masri. 2025-06-05. Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS. https://arxiv.org/abs/2506.04902

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