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Monitoring energy behaviors in AI data centers is crucial, both to reduce their energy consumption and to raise awareness among their users which are key actors in the AI field. This paper shows a proof of concept of easy and lightweight…
Recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies help ecosystem monitoring to shift towards automated monitoring with low power sensors and embedded vision on powerful processing units. Vision-based…
This paper introduces H-STREAM, a big stream/data processing pipelines evaluation engine that proposes stream processing operators as micro-services to support the analysis and visualisation of Big Data streams stemming from IoT (Internet…
While the environmental impact of digitalization is becoming more and more evident, the climate crisis has become a major issue for society. For instance, data centers alone account for 2.7% of Europe's energy consumption today. A…
In recent years, the monitoring and study of natural hazards have gained significant attention, particularly due to climate change, which exacerbates incidents like floods, droughts, storm surges, and landslides. Together with the constant…
Data is a valuable asset, and sharing it as a product across organizations is key to building comprehensive and useful insights in fields such as science and industry. Before sharing, data often requires transformation to comply with…
Measuring energy consumption is a challenging task faced by developers when building mobile apps. This paper presents EMaaS: a system that provides reliable energy measurements for mobile applications, without requiring a complex setup. It…
Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and…
This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence…
The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important…
Cloud-native software delivery platforms orchestrate releases through complex, multi-stage pipelines composed of dozens of independently versioned tasks. When code is promoted between environments -- development to staging, staging to…
Fluid antenna multiple access (FAMA) is capable of exploiting the high spatial diversity of wireless channels to mitigate multi-user interference via flexible port switching, which achieves a better performance than traditional…
In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms combined with energy harvesting solutions are crucial to obtain self-sustainability. This paper presents an energy-aware adaptive sampling rate…
In this paper, we propose FusionCIM, an operator-fusion-driven compute-in-memory (CIM) accelerator architecture for efficient and scalable LLM inference, with three key innovations: (1) a hybrid CIM pipeline architecture that maps QKT…
The growing global energy demand and the urgent need for sustainability call for innovative ways to boost energy efficiency. While advanced energy-saving systems exist, they often fall short without user engagement. Providing feedback on…
Microservices are often deployed and managed by a container orchestrator that can detect and fix failures to maintain the service availability critical in many applications. In Poll-based Container Monitoring (PCM), the orchestrator…
The ever-increasing size and computational complexity of today's machine-learning algorithms pose an increasing strain on the underlying hardware. In this light, novel and dedicated architectural solutions are required to optimize energy…
Current approaches to designing energy-efficient applications typically rely on measuring individual components using readily available local metrics, like CPU utilization. However, these metrics fall short when applied to cloud-native…
Test-time adaptation (TTA) aims to transfer knowledge from a source model to unknown test data with potential distribution shifts in an online manner. Many existing TTA methods rely on entropy as a confidence metric to optimize the model.…
Operationalizing AI has become a major endeavor in both research and industry. Automated, operationalized pipelines that manage the AI application lifecycle will form a significant part of tomorrow's infrastructure workloads. To optimize…