Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909639317127168 |
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| author | Pradeep, Preethika Al-Masri, Eyhab |
| author_facet | Pradeep, Preethika Al-Masri, Eyhab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04902 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS Pradeep, Preethika Al-Masri, Eyhab Distributed, Parallel, and Cluster Computing Performance Systems and Control 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. |
| title | Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS |
| topic | Distributed, Parallel, and Cluster Computing Performance Systems and Control |
| url | https://arxiv.org/abs/2506.04902 |