NeuroScaler: Towards Energy-Optimal Autoscaling for Container-Based Services

TL;DR

NeuroScaler is an AI-driven autoscaling system that optimizes energy and carbon efficiency in container-based services by integrating multi-tier telemetry and predictive control, reducing energy use significantly while maintaining performance.

Contribution

It introduces NeuroScaler, a novel energy-aware autoscaling framework that combines multi-tier telemetry with machine learning for energy optimization in cloud and edge networks.

Findings

Reduces energy consumption by 34.68% compared to HPA.

Maintains target latency during energy optimization.

Supports real-time telemetry and machine learning pipelines.

Abstract

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.