ITEC@AAU coordinates the project Kärntner Fog
The project “Kärntner Fog: A 5G-Enabled Fog Infrastructure for Automated Operation of Carinthia’s 5G Playground Application Use Cases” proposes a new infrastructure automation use case in the 5G Playground Carinthia (5GPG). Kärntner Fog plans to create and deploy a
distributed service middleware infrastructure over a diverse set of novel heterogeneous 5G edge devices, complemented by a high-performance Cloud data center accessible with low latency according to 5G standards. Such an infrastructure is currently missing in the 5GPG and will represent a horizontal backbone that interconnects and integrates the application use cases. Kärtner Fog will automate the development and operation of the applications use cases in the 5GPG in an integrated and more cost-effective fashion to enable more science and innovation within a limited budget.
Involved Organisations: BABEG, ITEC@AAU, ONDA TLC GmbH, FFG/KWF
Coordinator: Prof. Radu Prodan
Project Start: 01.01.2021
Project Duration: 48 months
Paper accepted in IEEE UCC
IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) accepted the paper “Dynamic Multi-objective Scheduling of Microservices in the Cloud”.
Authors: Hamid Mohammadi Fard, Radu Prodan, Felix Wolf
Abstract: For many applications, a microservices architecture promises better performance and flexibility compared to a conventional monolithic architecture. In spite of the advantages of a microservices architecture, deploying microservices poses various challenges for service developers and providers alike. One of these challenges is the efficient placement of microservices on the cluster nodes. Improper allocation of microservices can quickly waste resource capacities and cause low system throughput. In the last few years, new technologies in orchestration frameworks, such as the possibility of multiple schedulers for pods in Kubernetes, have improved scheduling solutions of microservices but using these technologies needs to involve both the service developer and the service provider in the behavior analysis of workloads. Using memory and CPU requests specified in the service manifest, we propose a general microservices scheduling mechanism that can operate efficiently in private clusters or enterprise clouds. We model the scheduling problem as a complex variant of the knapsack problem and solve it using a multi-objective optimization approach. Our experiments show that the proposed mechanism is highly scalable and simultaneously increases utilization of both memory and CPU, which in turn leads to better throughput when compared to the state-of-the-art.
IEEE DeSE 2020: Keynote Speaker
Prof. Radu Prodan is a keynote speaker at the 13th International Conference On The Developments in eSystems Engineering (DeSE), 13th-17th December 2020.
SOFA 2020: Guest of Honor and Talk
Prof. Radu Prodan ist guest of honor at the 9th International Workshop on Soft Computing Applications (SOFA), 27-29 Nov 2020, Arad, Romania. The title of his talk is “Distribute one Billion”.
Project ADAPT: Newspaper Article in “Kronen Zeitung”
The newspaper “Kronen Zeitung” published the article “IM KAMPF GEGEN CORONA: Universität Klagenfurt forscht mit den Chinesen” with Prof. Radu Prodan.
Project ADAPT: newspaper article in “Kleine Zeitung”
The newspaper “Kleine Zeitung” published the article “Medizinische Schutzausrüstung: Neue IT-Lösung soll Menschenleben retten” with Prof. Radu Prodan.
Paper accepted in the 10th IEEE Conference on Big Data and Cloud Computing: “Cloud — Edge Offloading Model for Vehicular Traffic Analysis”
Authors: Dragi Kimovski, Dijana C. Bogatinoska, Narges Mehran, Aleksandar Karadimce, Natasha Paunkoska, Radu Prodan, Ninoslav Marina
Abstract: The proliferation of smart sensing and computing devices, capable of collecting a vast amount of data, has made the gathering of the necessary vehicular traffic data relatively easy. However, the analysis of these big data sets requires computational resources, which are currently provided by the Cloud Data Centers. Nevertheless, the Cloud Data Centers can have unacceptably high latency for vehicular analysis applications with strict time requirements. The recent introduction of the Edge computing paradigm, as an extension of the Cloud services, has partially moved the processing of big data closer to the data sources, thus addressing this issue. Unfortunately, this unlocked multiple challenges related to resources management. Therefore, we present a model for scheduling of vehicular traffic analysis applications with partial task offloading across the Cloud — Edge continuum. The approach represents the traffic applications as a set of interconnected tasks composed into a workflow that can be partially offloaded to the Edge. We evaluated the approach through a simulated Cloud — Edge environment that considers two representative vehicular traffic applications with a focus on video stream analysis. Our results show that the presented approach reduces the application response time up to eight times while improving energy efficiency by a factor of four.