Rui Silva

Alisson O. Chaves, Rodrigo Moreira, Larissa F. Rodrigues Moreira et al.

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.

Rares Dolga, Ran Zmigrod, Rui Silva et al.

Log analysis and monitoring are essential aspects in software maintenance and identifying defects. In particular, the temporal nature and vast size of log data leads to an interesting and important research question: How can logs be summarised and monitored over time? While this has been a fundamental topic of research in the software engineering community, work has typically focused on heuristic-, syntax-, or static-based methods. In this work, we suggest an online semantic-based clustering approach to error logs that dynamically updates the log clusters to enable monitoring code error life-cycles. We also introduce a novel metric to evaluate the performance of temporal log clusters. We test our system and evaluation metric with an industrial dataset and find that our solution outperforms similar systems. We hope that our work encourages further temporal exploration in defect datasets.