Distributed and Parallel Systems

Prof. Radu Prodan

Radu Prodan talks about “Democratic Trustworthy News in the Social Continuum: the ARTICONF Appeoach” Live streaming

Radu Prodan talks about “Democratic Trustworthy News in the Social Continuum: the ARTICONF Appeoach” Live streaming at the Digital Day in Messina.


5G Kärntner Fog project: Kick-off meeting in Klagenfurt

The kick-off meeting of the “5G-KärntnerFog” Project took place on April, 21st, 2022 at Klagenfurt University. The purpose of this first meeting was primarily the definition of work structures, work packages, and getting to know each partner region. The project partners consist of the following institutions: ITEC (Lead), FH Kärnten, and Siplan. 

Paper accepted in the IEEE Transactions on Network and Service Management

Title: FaaScinating Resilience for Serverless Function Choreographies in Federated Clouds

Authors: Sasko Ristov, Dragi Kimovski, Thomas Fahringer

Abstract: Cloud applications often benefit from deployment on serverless technology Function-as-a-Service (FaaS), which may instantly spawn numerous functions and charge users for the period when serverless functions are running. Maximum benefit is achieved when functions are orchestrated in a workflow or function choreographies (FCs). However, many provider limitations specific for FaaS, such as maximum concurrency or duration often increase the failure rate, which can severely hamper the execution of entire FCs. Current support for resilience is often limited to function retries or try-catch, which are applicable within the same cloud region only. To overcome these limitations, we introduce rAF CL, a middleware platform that maintains the reliability of complex FCs in federated clouds. In order to support resilient FC execution under rAF CL, our model creates an alternative strategy for each function based on the required availability specified by the user. Alternative strategies are not restricted to the same cloud region, but may contain alternative functions across five providers, invoked concurrently in a single alternative plan or executed subsequently in multiple alternative plans. With this approach, rAF CL offers flexibility in terms of cost-performance trade-off. We evaluated rAF CL by running three real-life applications across three cloud providers. Experimental results demonstrated that rAF CL outperforms the resilience of AWS Step Functions, increasing the success rate of the entire FC by 53.45%, while invoking only 3.94% more functions with zero wasted function invocations. rAF CL significantly improves the availability of entire FCs to almost 1 and survives even after massive failures of alternative functions


Improve and accelerate how we learn from health data: New approach reduces machine learning time by 60%

Electronic health records, like ELGA in Austria, provide an overview of laboratory results, diagnostics and therapies. Much could be learned from the personal and private data of individuals – with the help of machine learning – for use in the treatment of others. However, the use of the data is a delicate matter, especially when it comes to diseases that carry a stigma. Researchers involved in the EU project “Enabling the Big Data Pipeline Lifecycle on the Computing Continuum (DataCloud)” are working to make new forms of information processing suitable for medical purposes. Dragi Kimovski and his colleagues recently presented their findings in a publication. Read the complete article here.


Editorial published in Wiley “Journal of Software” : Big data analytics in Industry 4.0 ecosystems

Title: Big data analytics in Industry 4.0 ecosystems

Authors: Gagangeet Singh Aujla, Radu Prodan, Danda B. Rawat

Journal: “Software: Practice and Experience”

Full editorial/article: https://onlinelibrary.wiley.com/doi/10.1002/spe.3008


Second online meeting between Austria and China took place on 21.02.2022

Second online meeting between Austria and China took place on 21.02.2022. Consortium discussed aspects of sustainable transportation networks, #blockchain and new development strategies in line with #UnitedNations #IYBSSD #SDGs

Prof. Radu Prodan

Paper accepted in IEEE Computer Magazine: Big Data Pipelines on the Computing Continuum: Tapping the Dark Data

Title: Big Data Pipelines on the Computing Continuum: Tapping the Dark Data


Authors: Dumitru Roman, Radu Prodan, Nikolay Nikolov, Ahmet Soylu, Mihhail Matskin, Andrea Marrella, Dragi Kimovski, Brian Elvesæter, Anthony Simonet-Boulogne, Giannis Ledakis, Hui Song, Francesco Leotta, Evgeny Kharlamov


Abstract: Big Data pipelines are essential for leveraging Dark Data, i.e., data collected but not used and turned into value. However, tapping their potential requires going beyond existing approaches and frameworks for Big Data processing. The Computing Continuum enables new opportunities for managing Big Data pipelines concerning efficient management of heterogeneous and untrustworthy resources. This article discusses the Big Data pipelines lifecycle on the Computing Continuum, its associated challenges and outlines a future research agenda in this area.

Prof. Radu Prodan
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Exploit 5G potential: Research team builds “Carinthian Fog”

In Carinthia, researchers find an open test laboratory in the 5G Playground Carinthia, where the possibilities of the new mobile phone technology can be explored. The problem is: 5G enables the fast transmission of large amounts of data, but these also have to be processed. Read the whole interview of Univ.-Prof. DI Dr. Radu Prodan in the latest University Klagenfurt news.

Dragi Kimovski

Paper accepted in the IEEE Computer Magazine: Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

Title: Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

Authors: Dragi Kimovski, Sasko Ristov, Radu Prodan

Abstract: The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research, or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.