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Device-Edge-Cloud Intelligent Collaboration framEwork

Descrizione del progetto

Un quadro di gestione del cloud aperto e portatile a pianificazione intelligente

Il cloud computing è cresciuto enormemente nell’ultimo decennio, di pari passo con l’ascesadi nuove applicazioni che si basano su hardware specifico. Nel frattempo, le esigenze degli utenti, come la sicurezza e la consapevolezza della posizione, stanno diventando diffuse nelle città intelligenti, nell’automazione industriale e nell’analisi dei dati. Inoltre, le moderne applicazioni cloud sono complicate e richiedono un nuovo quadro di gestione. In questo contesto, il progetto DECICE, finanziato dall’UE, ottimizzerà il posizionamento dei carichi di lavoro in un panorama hardware eterogeneo che comprende cloud, edge e HPC. Il progetto, che riunisce 13 partner provenienti da Austria, Germania, Italia, Svezia, Turchia e Regno Unito, utilizzerà un gemello digitale del sistema per creare un ambiente di formazione virtuale finalizzato a testare i dati per l’addestramento di modelli di apprendimento automatico e l’esplorazione di scenari «what-if».

Obiettivo

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.

Meccanismo di finanziamento

RIA - Research and Innovation action

Coordinatore

GEORG-AUGUST-UNIVERSITAT GOTTINGEN STIFTUNG OFFENTLICHEN RECHTS
Contribution nette de l'UE
€ 687 500,00
Indirizzo
WILHELMSPLATZ 1
37073 Gottingen
Germania

Mostra sulla mappa

Regione
Niedersachsen Braunschweig Göttingen
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Costo totale
€ 687 500,00

Partecipanti (11)

Partner (1)