NeuroScaler：迈向容器化服务的能源最优自动扩展
NeuroScaler: Towards Energy-Optimal Autoscaling for Container-Based Services
AI总结：
NeuroScaler通过整合多层级 telemetry 数据，结合机器学习模型预测控制策略，实现容器化服务的能源最优自动扩展，降低能耗34.68%同时保持目标延迟。
摘要：
未来网络必须在严格能量和碳约束下满足严格要求。当前自动扩展机制仍以工作负载为中心且基础设施隔离，且大多不了解其环境影响。我们提出了NeuroScaler，一种针对绿色云和边缘网络的AI原生、节能且碳感知的调度器。NeuroScaler聚合多层级 telemetry 数据，从电力分配单元（PDUs）通过裸金属服务器到由Kubernetes管理的虚拟化基础设施，使用每层级不同的能量和计算指标。它支持多个机器学习流水线，将负载、性能和功率联系起来。在这一统一的可观测性层中，模型预测控制策略优化能源使用，同时满足服务级别目标。在具有生产级服务器支持真实服务的真实测试床上，NeuroScaler相比水平pod自动扩展器（HPA）将能耗降低了34.68%，同时保持目标延迟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
