Title: Serverless ECG Stream Processing in Federated Clouds with Lambda Architecture

Authors: Sashko Ristov, Marjan Gusev, Armin Hohenegger, Radu Prodan, Dimitar Mileski, Pano Gushev, Goran Temelkov

Computer Jounal, Special Issue on Computing in Telemedicine

Abstract: Although telemedicine has emerged as an everyday necessity for health monitoring, still current solutions are built for a small number of patients or use sensors that do not stream data with high velocity and volume. In this article, we explore a novel architecture for distributing  health monitoring computations over distributed cloud regions, both for constantly online patients and offline for several hours daily. We propose a conceptual architecture for a use-case example capable of processing thousands of simultaneous incoming streams with electrocardiogram signals. Current serverless cloud providers limit the concurrency within a single region, and we evaluate the performance of this solution across multiple cloud regions. The results indicate that our new solution can overcome the limitations of a single cloud for online and offline patients, thereby saving their lives in case of detected dangerous arrhythmia.

Authors: Martin Molan, Junaid Ahmed Khan, Andrea Bartolini, Roberta Turra, Giorgio Pedrazzi, Michael Cochez, Alexandru Iosup, Dumitru Roman, Jože Rožanec, Ana Lucia Vărbănescu, Radu Prodan

COMPSAC 2023: 1st IEEE International Workshop on Digital Twins for Metaverse

Abstract: Modeling and understanding an expensive next-generation data center operating at a sustainable exascale performance remains a challenge yet to solve. The paper presents the approach taken by the Graph-Massivizer project, funded by the European Union, towards a sustainable data center, targeting a massive graph representation and analysis of its digital twin. We introduce five interoperable open-source tools that support this undertaking, creating an automated, ustainable loop of graph creation, analytics, optimization, sustainable resource management, and operation, emphasizing state-of-the-art progress. We plan to employ the tools for designing a massive data center graph, representing a digital twin describing spatial, semantic, and temporal relationships between the monitoring metrics, hardware nodes, cooling equipment, and jobs. The project aims to strengthen Bologna Technopole as a leading European supercomputing and big data hub offering sustainable green computing for improved societally relevant science throughput.

Authors: Akif Quddus Khan, Nikolay Nikolov, Mihhail Matskin‡, Radu Prodan, Christoph Bussler, Dumitru Roman, Ahmet Soylu

COMPSAC 2023: 8th IEEE International Workshop on Distributed Big Data Management

Abstract: Cloud computing has become an increasingly popular choice for businesses and individuals due to its flexibility, scalability, and convenience; owever, the rising cost of cloud resources has become a significant concern for many. The pay-per-use model used in cloud computing means that costs can accumulate quickly, and the lack of visibility and control can result in unexpected expenses. The cost structure becomes even more complicated when dealing with hybrid or multi-cloud environments. For businesses, the cost of cloud computing can be a significant portion of their IT budget, and any savings can lead to better financial stability and competitiveness. In this respect, it is essential to manage cloud costs effectively. This requires a deep understanding of current resource utilization, forecasting future needs, and optimising resource utilization to control costs. To address this challenge, new tools and techniques are being developed to provide more visibility and control over cloud computing costs. In this respect, this paper explores a graph-based solution for modelling cost elements and cloud resources and potential ways to solve the resulting constraint problem of cost optimisation. We primarily consider utilization, cost, performance, and availability in this context. Such an approach will eventually help organizations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.

Dragi Kimovski  participated in the EU Concentration and Consultation event on the computing continuum, held in Brussels on May 10-11, 2023. During the event, he delivered a presentation on the groundbreaking DataCloud H2020 Project and actively engaged in open panels and poster sessions.

Titel: A distributed geofence-based discovery scheme for the computing Continuum

Euro-Par 2023, https://2023.euro-par.org/

Authors: Kurt Horvath1, Dragi Kimovski1, Christoph Uran1,2, Helmut Wöllik2, and
Radu Prodan1

1 Institute of Information Technology, University Klagenfurt, Klagenfurt, Austria

2 Faculty of Engineering and IT, Carinthian University of Applied Science
Klagenfurt, Austria surname@fh-kaernten.at

Abstract: Service discovery is a vital process that enables low latency provisioning of Internet of Things applications across the computing continuum. Unfortunately, it becomes increasingly difficult to identify a proper service within strict time constraints due to the high heterogeneity of the computing continuum. Moreover, the plethora of network technologies and protocols commonly used by the Internet of Things applications further hinders service discovery. To address these issues, we introduce a novel mobile edge service discovery algorithm named Mobile Edge Service Discovery using the DNS (MESDD), which utilizes intermediate code to identify a suitable service instance across the computing continuum based on the naming scheme used to identify the users’ location. MESDD utilizes geofences to aid this process, which enables fine-grained resource discovery. We deployed a real-life distributed computing continuum testbed and compared MESDD with three related methods. The evaluation results show that MESDD outperforms the other approaches by 60% after eight discovery iterations.


Title: Action Recognition in Video Recordings from Gynecologic Laparoscopy

Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Daniela Stefanics, Klaus Schoeffmann and Heinrich Husslein

IEEE 36th International Symposium on Computer-Based Medical Systems 2023

Abstract: Action recognition is a prerequisite for many applications in laparoscopic video analysis including but not limited to surgical training, operation room planning, follow-up surgery preparation, post-operative surgical assessment, and surgical outcome estimation. However, automatic action recognition in laparoscopic surgeries involves numerous challenges such as (I) cross-action and intra-action duration variation, (II) relevant content distortion due to smoke, blood accumulation, fast camera motions, organ movements, object occlusion, and (III) surgical scene variations due to different illuminations and viewpoints. Besides, action annotations in laparoscopy surgeries are limited and expensive due to requiring expert knowledge. In this study, we design and evaluate a CNN-RNN architecture as well as a customized training-inference framework to deal with the mentioned challenges in laparoscopic surgery action recognition. Using stacked recurrent layers, our proposed network takes advantage of inter-frame dependencies to negate the negative effect of content distortion and variation in action recognition. Furthermore, our proposed frame sampling strategy effectively manages the duration variations in surgical actions to enable action recognition with high temporal resolution. Our extensive experiments confirm the superiority of our proposed method in action recognition compared to static CNNs.

Authors: Clemens SAUERWEIN1 (Innsbruck), Ruth BREU (Innsbruck), Stefan OPPL (Krems), Iris GROHER (Linz), Tobias ANTENSTEINER (Innsbruck), Stefan PODLIPNIG (Wien) & Radu PRODAN (Klagenfurt)

Abstact: High-quality programming education at universities is a significant challenge due to rapidly increasing student numbers, tight teaching budgets and a shortage of instructors. The “CodeAbility Austria” project aims to meet this challenge by establishing suitable programming learning platforms. In this paper, we introduce the project in more detail, present the results of our empirical research on the experiences and challenges of using programming learning platforms, and provide an outlook for future work.

Link: Zeitschrift für Hochschulentwicklung



As part of our collaboration with the Department of Computer Architecture and Technology at the University of Granada, Spain two research papers were accepted for publishing at the 17th International Work-Conference on Artificial Neural Networks.

Paper title:  An Efficient Parallel Multi-population Wrapper for Solving Feature Selection Problems in High-dimensional Space

Authors: Juan Carlos Gómez López, Daniel Castillo Secilla, Dragi Kimoviski and Jesús González Peñalver

Abstract: One of the most widely accepted approaches to address feature selection problems are wrappers based on evolutionary algorithms. Over the years, these approaches have evolved from single-population models to multi-population models to achieve better-quality solutions. Moreover, they are highly parallelizable as each subpopulation evolves independently. This paper proposes two parallel strategies for a multi-population wrapper to take advantage of a multicore CPU. The first one, based on the Fork/Join model, focuses on parallelizing only the evaluation method since it is the main bottleneck of the procedure. Although this strategy speeds up the execution of the wrapper, it is far from optimal. In this context, the second strategy implements an Island-based model, where each subpopulation evolves independently in each CPU core, exchanging information via asynchronous migrations. The results show that the wrapper achieves a speedup of almost 35 with the Island-based model when individuals are distributed into 24 subpopulations.


Paper title: Energy-aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms

Authors: Juan José Escobar Pérez, Francisco Rodríguez, Rukiye Savran Kızıltepe, Beatriz Prieto, Dragi Kimovski, Andrés Ortiz, Alberto Prieto and Miguel Damas

Abstract: The growing energy consumption caused by IT is forcing application developers to consider energy efficiency as one of the fundamental design parameters. This parameter acquires great relevance in HPC systems when running artificial neural networks and Machine Learning applications. Thus, this article shows an example of how to estimate and consider energy consumption in a real case of EEG classification. An efficient and distributed implementation of the KNN algorithm that uses mRMR as a feature selection technique to reduce the dimensionality of the dataset is proposed. The performance of three different workload distributions is analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works. It achieves an accuracy rate of 88.8% and a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy of the sequential version.

Zahra Najafabadi Samani, has been awarded travel grant to attend IPDPS 2023 in St. Petersburg, Florida, USA. Congratulations!

Mathias Lux

Creating games for 48 hours at 1770 meters of altitude. The 2nd Hüttenjam took place April 13-16, 2023, on the Turracher Höhe. After settling on a topic for the jam on Thursday – extreme conditions – 40 participants worked on 8 games to be presented on Saturday evening. The jam took place in a chilled atmosphere and allowed for networking, winter sports, a sauna, and a lot of creative space. A video summary of the event is available at https://youtu.be/8IwSBsk-3Fc You can also play the games developed there at https://itch.io/jam/2nd-huettenjam
The Hüttenjam is a joint event of Game Dev Graz and the University of Klagenfurt / ITEC: https://www.xn--httenjam-65a.at-