Device-Edge-Cloud Intelligent Collaboration framEwork

Smart Computing

DECICE aims to develop an AI-based, open and portable cloud management framework for automatic and adaptive optimization and deployment of applications in a federated infrastructure, including computing from the very large (e.g., HPC systems) to the very small (e.g., IoT sensors connected on the edge).




01/12/2022 - 30/11/2025

Projektbudget in EUR


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.