Description du projet
Un cadre de gestion de l’informatique en nuage ouvert et portable avec une planification intelligente
L’informatique en nuage a connu un essor considérable au cours de la dernière décennie. Cette augmentation s’est accompagnée d’une hausse des nouvelles applications qui s’appuient sur du matériel spécialisé. Parallèlement à cela, les exigences des utilisateurs en matière de sécurité et de localisation deviennent courantes dans les villes intelligentes, l’automatisation industrielle et l’analyse des données. En outre, les applications cloud modernes sont complexes et nécessitent un nouveau cadre de gestion du nuage. Dans ce contexte, le projet DECICE, financé par l’UE, optimisera le placement des charges de travail dans le paysage matériel hétérogène incluant le cloud, la périphérie de réseau et le CHP (calcul haute performance). Réunissant 13 partenaires d’Autriche, d’Allemagne, d’Italie, de Suède, de Turquie et du Royaume-Uni, le projet utilisera un jumeau numérique du système pour créer un environnement d’entraînement virtuel afin de tester des données pour l’entraînement de modèles d’apprentissage automatique et l’exploration de scénarios de simulation.
Objectif
The cloud computing industry has grown massively over the last decade and with that new areas of application have arisen. Some areas require specialized hardware, which needs to be placed in locations close to the user. User requirements such as ultra-low latency, security and location awareness are becoming more and more common, for example, in Smart Cities, industrial automation and data analytics. Modern cloud applications have also become more complex as they usually run on a distributed computer system, split up into components that must run with high availability.
Unifying such diverse systems into centrally controlled compute clusters and providing sophisticated scheduling decisions across them are two major challenges in this field. Scheduling decisions for a cluster consisting of cloud and edge nodes must consider unique characteristics such as variability in node and network capacity. The common solution for orchestrating large clusters is Kubernetes, however, it is designed for reliable homogeneous clusters. Many applications and extensions are available for Kubernetes. Unfortunately, none of them accounts for optimization of both performance and energy or addresses data and job locality.
In DECICE, we develop an open and portable cloud management framework for automatic and adaptive optimization of applications by mapping jobs to the most suitable resources in a heterogeneous system landscape. By utilizing holistic monitoring, we construct a digital twin of the system that reflects on the original system. An AI-scheduler makes decisions on placement of job and data as well as conducting job rescheduling to adjust to system changes. A virtual training environment is provided that generates test data for training of ML-models and the exploration of what-if scenarios. The portable framework is integrated into the Kubernetes ecosystem and validated using relevant use cases on real-world heterogeneous systems.
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RIA - Research and Innovation actionCoordinateur
37073 Gottingen
Allemagne