Green by Design: Constraint-Based Adaptive Deployment in the Cloud Continuum
Andrea D'Iapico, Monica Vitali
TL;DR
The paper tackles the dynamic environmental impact of cloud-native applications deployed across the cloud-edge continuum by introducing automatic generation of green-aware deployment constraints learned from continuous monitoring. It presents a methodology and architecture centered on a Green-aware Constraint Generator, energy Estimator, and Knowledge Base, all feeding a Human-In-The-Loop Explainability framework to guide environmentally sensitive deployment decisions. Key contributions include a modular constraint library with AvoidNode and Affinity rules, a data-driven thresholding mechanism with adaptive tau, and an explainability artifact that quantifies potential emission reductions for DevOps practitioners. The approach is validated on a benchmark Online Boutique deployment, demonstrating adaptive constraint generation across varied infrastructure and workload contexts and scalability to larger deployments. The work improves practical deployment planning by enabling energy-aware, context-adaptive scheduling decisions with transparent rationales.
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.