Experimenting with Energy-Awareness in Edge-Cloud Containerized Application Orchestration

2025-11-12

AI Summary

To address high energy consumption of containerized applications and the lack of fine-grained energy awareness in resource scheduling within heterogeneous edge-cloud environments, this paper proposes an embedded energy-aware scheduling framework. The framework integrates real-time power consumption metrics across both computation and networking dimensions into the Kubernetes scheduler and implements dynamic energy-efficiency optimization on an ARM-based physical edge testbed. Its key innovations include a lightweight hardware-coordinated monitoring mechanism and a redesigned scheduling decision logic that jointly optimizes workload distribution and energy consumption. Experimental results demonstrate that, under high-load conditions, the proposed approach reduces total system energy consumption by 23.7% compared to vanilla Kubernetes, while maintaining QoS guarantees and high resource utilization—thereby significantly enhancing the energy efficiency of edge-cloud collaborative systems.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper explores the role of energy-awareness strategies into the deployment of applications across heterogeneous Edge-Cloud infrastructures. It proposes methods to inject into existing scheduling approaches energy metrics at a computational and network level, to optimize resource allocation and reduce energy consumption. The proposed approach is experimentally evaluated using a real-world testbed based on ARM devices, comparing energy consumption and workload distribution against standard Kubernetes scheduling. Results demonstrate consistent improvements in energy efficiency, particularly under high-load scenarios, highlighting the potential of incorporating energy-awareness into orchestration processes for more sustainable cloud-native computing.

Problem

• Optimizing energy-aware container orchestration in Edge-Cloud infrastructures
• Injecting energy metrics into scheduling for resource allocation efficiency
• Reducing energy consumption in Kubernetes through experimental ARM-based evaluation

Innovation

• Inject energy metrics into scheduling approaches
• Optimize resource allocation across Edge-Cloud infrastructures
• Evaluate using ARM-based testbed against Kubernetes scheduling