The rapid growth of cloud data centres has increased their energy consumption and environmental footprint, highlighting the need for more energy-efficient resource management. Kubernetes has become a widely adopted container orchestration platform for automating the deployment, scaling, and management of containerized workloads. This study investigates the impact of Kubernetes scheduling decisions on cluster power consumption. A fine-grained monitoring system was implemented to characterize application behaviour and cluster state by collecting metrics at the node, shared-resource, and container levels. Controlled pod-placement scenarios were designed to evaluate how workload distribution topology, microservice affinity, and resource contention affect power consumption. Using the collected dataset, an XGBoost model was developed to predict cluster power consumption associated with pod placement based on pre-scheduling system-state metrics. The model achieved an (R2) score of 93.2%, demonstrating high predictive accuracy. Building on these results, future work will focus on developing a customized Kubernetes scheduler based on reinforcement learning and integrating the power-prediction model to enable energy-aware pod placement. The proposed approach aims to support more sustainable and energy-efficient cloud data centre operations.
Megi Tartari, Genti Daci, Elinda Kajo Meçe· Ajakirjad. Journals by UT· 0 citations
Intracranial aneurysm rupture is a major cause of hemorrhagic stroke and a significant contributor to human mortality. With the emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), a series of new approaches have been introduced to improve aneurysm detection, rupture assessment, risk prediction, and prognosis evaluation. This narrative review synthesizes the findings from 21 peer-reviewed studies identified through searches in PubMed, Scopus, Web of Science, and IEEE Xplore, covering the period from June to September 2025. The reviewed studies examined the use of AI for aneurysm detection, rupture assessment and risk prediction, and prognosis evaluation based on clinical, morphological, radiomic, and hemodynamic data. The reviewed studies applied different AI approaches, including conventional ML algorithms, convolutional neural networks, transformer-based models, radiomics, and multimodal models. Several studies reported encouraging performance in aneurysm detection, segmentation, rupture assessment, and prognosis evaluation, while some approaches showed better performance compared with traditional clinical scoring systems. However, important limitations remain, including the frequent use of small or single-center datasets, limited external validation, and heterogeneity in imaging protocols and feature-extraction methods. AI is showing strong potential in supporting the management of intracranial aneurysms. However, further research is needed, particularly through multicenter and external validation, improved model explainability and robustness, and better integration of these systems into clinical workflows.
Olti Qirici, Eugen Enesi, Kleona Binjaku et al.· Diagnostics· 0 citations
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