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
name.surname@aau.at

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-

 

Radu Prodan participated in the panel:

“Computing in the Cloud Continuum: Technological challenges, killer applications and future trends”

jointly organized by the Fast Continuum and HotCloudPerf workshops at the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23).

6th Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf 2023)

Radu ProdanFour Hot Topics in Cloud Computing Performance in Klagenfurt 

Description: The presentation discusses four hot Cloud computing topics researched at the University of Klagenfurt:
– Social media as today’s largest and most popular front-end application worldwide;
– Fine-grained simulation of backend serverless functions workflows on commercial clouds;
– Scheduling of workflow applications on the computing continuum assuring service level agreements;
– Sustainable processing of the massive graph representation of extreme data generated on the Internet. 

On Saturday, during the #ICPE2023, the Graph-Massivizer Project organized the #GraphSys first #workshop on #Serverless, #ExtremeScale, and #Sustainable #GraphProcessing #Systems

It was great to see so many passionate #attendees eager to share and learn about the latest #advancements in #graphsystems ?️ We had some amazing speakers who shared their #insights and #expertise on the topic with a lot of engaging and thought-provoking discussions ⚡ ??Thanks to everyone who participated and made it such a memorable event!

Title: Performance Improvement Strategies of Edge-Enabled Social Impact Applications

Authors: Shajulin Benedict, S. Vivek Reddy, Bhagyalakshmi M., Jiby Mariya Jose, Radu Prodan

International Conference on Inventive Computation Technologies (ICICT 2023)

Abstract: In recent years, social relationships have been rooted in a blend with technological advancements to eradicate emerging challenges, such as loneliness, poverty, pollution, climate change, health issues, and so forth. IoT-enabled social good applications, accordingly, have emerged in various dimensions. In fact, those developing IoT-enabled social good applications have to diligently consider the efficiency of underlying computational infrastructures. This article explores the performance improvement (PI) aspects of edge intelligence techniques that apply to social good applications. It highlights the most commonly practiced PI methods in the literature. Additionally, the article lists the near-future research perspectives of edge-enabled solutions. The article
will be beneficial to several researchers/practitioners who prefer to address social causes using edge-enabled efficient intelligent techniques.