Green by Design: Constraint-Based Adaptive Deployment in the Cloud Continuum

2026-02-20

AI Summary

This work addresses the challenge of balancing computational demands and environmental impact in deploying cloud-native applications across the cloud-edge continuum, where existing approaches often result in excessive energy consumption and carbon emissions. The authors propose a green-aware adaptive deployment mechanism that continuously analyzes component communication patterns, node-level energy consumption profiles, and regional carbon intensity to automatically learn and dynamically update green constraints. These constraints guide the scheduler toward generating low-carbon deployment configurations. By integrating a monitoring data-driven constraint generation algorithm with native cloud scheduling techniques, the proposed method significantly reduces both energy usage and carbon footprint in real-world scenarios, demonstrating its effectiveness and practical applicability.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The environmental sustainability of Information Technology (IT) has emerged as a critical concern, driven by the need to reduce both energy consumption and greenhouse gas (GHG) emissions. In the context of cloud-native applications deployed across the cloud-edge continuum, this challenge translates into identifying energy-efficient deployment strategies that consider not only the computational demands of application components but also the environmental impact of the nodes on which they are executed. Generating deployment plans that account for these dynamic factors is non-trivial, due to fluctuations in application behaviour and variations in the carbon intensity of infrastructure nodes. In this paper, we present an approach for the automatic generation of deployment plans guided by green constraints. These constraints are derived from a continuous analysis of energy consumption patterns, inter-component communication, and the environmental characteristics of the underlying infrastructure. This paper introduces a methodology and architecture for the generation of a set of green-aware constraints that inform the scheduler to produce environmentally friendly deployment plans. We demonstrate how these constraints can be automatically learned and updated over time using monitoring data, enabling adaptive, energy-aware orchestration. The proposed approach is validated through realistic deployment scenarios of a cloud-native application, showcasing its effectiveness in reducing energy usage and associated emissions.

Problem

• green computing
• cloud-edge continuum
• energy efficiency
• carbon intensity
• sustainable deployment

Innovation

• green-aware constraints
• adaptive deployment
• cloud-edge continuum
• carbon intensity
• energy-efficient orchestration