Descripción del proyecto
Un marco de gestión de la nube abierto y portátil con programación inteligente
La computación en nube ha crecido muchísimo en la última década. A este aumento se ha sumado un incremento de las nuevas aplicaciones que dependen de «hardware» especializado. Mientras tanto, los requisitos de los usuarios, como la seguridad y el reconocimiento de la ubicación, son cada vez más habituales en las ciudades inteligentes, la automatización industrial y el análisis de datos. Además, las aplicaciones modernas en la nube son complejas y requieren un nuevo marco de gestión de la nube. En este contexto, el equipo del proyecto DECICE, financiado con fondos europeos, optimizará la asignación de volúmenes de trabajo en un entorno de «hardware» heterogéneo que incluye la nube, la computación en el borde y la informática de alto rendimiento. El proyecto, que reúne a trece socios de Austria, Alemania, Italia, Suecia, Turquía y el Reino Unido, empleará un gemelo digital del sistema para crear un entorno de formación virtual que permita contrastar datos para el entrenamiento de modelos de aprendizaje automático y la exploración de escenarios hipotéticos.
Objetivo
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 actionCoordinador
37073 Gottingen
Alemania