Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS
Preethika Pradeep, Eyhab Al-Masri
TL;DR
GreenPod is a TOPSIS-based scheduler that significantly enhances energy efficiency in AIoT workloads on Kubernetes by considering multiple criteria, balancing sustainability and performance.
Contribution
This paper introduces GreenPod, a novel multi-criteria scheduler using TOPSIS for energy-efficient pod placement in heterogeneous AIoT environments.
Findings
GreenPod reduces energy consumption by up to 39.1%.
It performs well on medium complexity workloads.
GreenPod balances energy efficiency with scheduling latency.
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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Taxonomy
TopicsCloud Computing and Resource Management · Parallel Computing and Optimization Techniques · Big Data and Digital Economy