Research output per year
Research output per year
Project Details
Description
The goal of AORTA is to develop a framework that allows offloading of real-time services and
functionality to the edge and cloud, as well as integration of them with services in the edge/cloud.
The ambition is to support, for example advanced robotics or manufacturing applications in utilizing
non-local services in a predictable fashion. We will build upon recent advances in predictable communication and compute technologies, such as TSN, Kubernetes and 5G. A new real-time
computing platform consisting of a portable real-time virtual machine that supports dynamic code
migration for offloading.
functionality to the edge and cloud, as well as integration of them with services in the edge/cloud.
The ambition is to support, for example advanced robotics or manufacturing applications in utilizing
non-local services in a predictable fashion. We will build upon recent advances in predictable communication and compute technologies, such as TSN, Kubernetes and 5G. A new real-time
computing platform consisting of a portable real-time virtual machine that supports dynamic code
migration for offloading.
| Acronym | AORTA |
|---|---|
| Status | Active |
| Effective start/end date | 2023/01/01 → 2025/12/31 |
Collaborative partners
- Lund University
- Ericsson AB (lead)
- Mälardalen University
- Cognibotics AB
Funding
- Swedish Government Agency for Innovation Systems (Vinnova)
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Subject classification (UKÄ)
- Control Engineering
- Computer Systems
- Robotics and automation
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Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications
Raeiszadeh, M., Ebrahimzadeh, A., Glitho, R., Eker, J. & Mini, R., 2025 Jan 4, In: IEEE Transactions on Machine Learning in Communications and Networking. 3Research output: Contribution to journal › Article › peer-review
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Real-Time Adaptive Anomaly Detection in Industrial IoT Environments
Raeiszadeh, M., Ebrahimzadeh, A., Glitho, R., Eker, J. & Mini, R., 2024 Aug 23, In: IEEE Transactions on Network and Service Management.Research output: Contribution to journal › Article › peer-review
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Real-Time Anomaly Detection Using Distributed Tracing in Microservice Cloud Applications
Raeiszadeh, M., Ebrahimzadeh, A., Saleem, A., Glitho, R., Eker, J. & Mini, R., 2023 Nov 1, Proceeding of IEEE CloudNet 2023.Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review
Open AccessFile269 Downloads (Pure)
Projects
- 1 Active
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PhD student Project:Offloading using Edge, Cloud, 5G, and WebAssembly
Albayati, A. (Researcher)
2023/08/01 → 2028/08/01
Project: Research
Infrastructure
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RobotLab LTH Infrastructure
Krueger, V. (Manager), Olofsson, B. (Manager), Karayiannidis, Y. (Manager), Haage, M. (Manager), Malec, J. (Manager) & Topp, E. A. (Manager)
Faculty of Engineering, LTHInfrastructure