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

TL;DR

This paper proposes a methodology for automatically generating energy-efficient, environmentally conscious deployment plans for cloud-native applications across the cloud-edge continuum, adapting to dynamic conditions to reduce energy consumption and emissions.

Contribution

It introduces a novel approach for deriving and updating green constraints from monitoring data to guide adaptive, energy-aware deployment planning in cloud environments.

Findings

Reduces energy consumption in deployment scenarios

Effectively adapts to dynamic application and infrastructure changes

Demonstrates environmental benefits through realistic case studies

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.