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.

翻译：信息技术的环境可持续性已成为一个关键问题，这源于减少能源消耗和温室气体排放的需求。在部署于云-边连续体的云原生应用背景下，这一挑战转化为识别能效优化的部署策略，这些策略不仅需要考虑应用组件的计算需求，还需考虑执行这些组件的节点的环境影响。由于应用行为的波动以及基础设施节点碳强度的变化，生成考虑这些动态因素的部署计划并非易事。本文提出了一种由绿色约束引导的自动生成部署计划的方法。这些约束源于对能耗模式、组件间通信以及底层基础设施环境特征的持续分析。本文介绍了一种用于生成一组绿色感知约束的方法论和架构，这些约束为调度器提供信息，以产生环境友好的部署计划。我们展示了如何利用监控数据自动学习和更新这些约束，从而实现自适应的、能量感知的编排。通过云原生应用的真实部署场景验证了所提方法的有效性，展示了其在降低能源使用和相关排放方面的成效。