Future networks must meet stringent requirements while operating within tight energy and carbon constraints. Current autoscaling mechanisms remain workload-centric and infrastructure-siloed, and are largely unaware of their environmental impact. We present NeuroScaler, an AI-native, energy-efficient, and carbon-aware orchestrator for green cloud and edge networks. NeuroScaler aggregates multi-tier telemetry, from Power Distribution Units (PDUs) through bare-metal servers to virtualized infrastructure with containers managed by Kubernetes, using distinct energy and computing metrics at each tier. It supports several machine learning pipelines that link load, performance, and power. Within this unified observability layer, a model-predictive control policy optimizes energy use while meeting service-level objectives. In a real testbed with production-grade servers supporting real services, NeuroScaler reduces energy consumption by 34.68% compared to the Horizontal Pod Autoscaler (HPA) while maintaining target latency.

翻译：未来网络必须在严格的能量与碳排放约束下满足严苛的性能要求。现有弹性伸缩机制仍以工作负载为中心且受限于基础设施孤岛，大多未考虑其环境影响。本文提出NeuroScaler——一种面向绿色云边网络的人工智能原生、高能效且具备碳感知能力的编排器。该系统通过从配电单元（PDU）到裸机服务器，再到由Kubernetes管理的容器化虚拟基础设施的多层级遥测数据聚合，在各层级采用差异化的能量与计算度量指标。它支持多种关联负载、性能与功耗的机器学习流程。在此统一可观测层内，模型预测控制策略在满足服务等级目标的同时优化能量使用。在搭载生产级服务器并运行真实服务的实测平台中，NeuroScaler相较于水平Pod自动伸缩器（HPA）在保持目标延迟的同时降低了34.68%的能量消耗。