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Outstanding Poster Award (2nd Place) for Josef Hammer at the IPDPS PhD Forum 2023

Josef Hammer received the 2nd Place Outstanding Poster Award at the IPDPS PhD Forum 2023 for his poster titled “Distributed On-Demand Deployment for Transparent Access to 5G Edge Computing Services.” The event took place in St. Petersburg, Florida, USA, and was attended by Josef Hammer and Radu Prodan.

The recognition was part of the 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2023). For more information about the research and its contributors, visit the website: https://edge.itec.aau.at/.

 

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Josef Hammer presented his work at PAISE 2023

Josef Hammer presented the paper “Distributed On-Demand Deployment for Transparent Access to 5G Edge Computing Services” at the 5th Workshop on Parallel AI and Systems for the Edge (PAISE 2023). The workshop was held in conjunction with the 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2023) in St. Petersburg, Florida, USA.

Authors: Josef Hammer and Hermann Hellwagner, Alpen-Adria-Universität Klagenfurt

Abstract: Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network. Our previous publications argue that edge computing should be transparent to clients, leveraging Software-Defined Networking (SDN). While we introduced a solution to implement such a transparent approach, one question remained: How to handle user requests to a service that is not yet running in a nearby edge cluster? One advantage of the transparent edge is that one could process the initial request in the cloud. However, this paper argues that on-demand deployment might be fast enough for many services, even for the first request. We present an SDN controller that automatically deploys an application container in a nearby edge cluster if no instance is running yet. In the meantime, the user’s request is forwarded to another (nearby) edge cluster or kept waiting to be forwarded immediately to the newly instantiated instance. Our performance evaluations on a real edge/fog testbed show that the waiting time for the initial request – e.g., for an nginx-based service – can be as low as 0.5 seconds – satisfactory for many applications.

Josef Hammer presented his work at PAISE 2023

For more information about the research, visit the website: https://edge.itec.aau.at/.

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Paper accepted: Action Recognition in Video Recordings from Gynecologic Laparoscopy

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.

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Two papers accepted at the 17th International Work-Conference on Artificial Neural Networks.

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.

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Paper accepted: C3-Edge – An Automated Mininet-Compatible SDN Testbed On Raspberry Pis and Nvidia Jetsons

36th IEEE/IFIP Network Operations and Management Symposium (NOMS 2023) Miami, USA
Authors: Josef Hammer, Dragi Kimovski, Narges Mehran, Radu Prodan, and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)
Abstract: The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. While there is vast potential for research in these areas, managing complex adaptive infrastructure is difficult, and experiments with real hardware are tedious to set up. Furthermore, proposed solutions often require expensive hardware or labor-intensive procedures to replicate and build on these ideas. With our C3-Edge testbed, we address these challenges and propose a novel approach for automated edge testbed setup with a low-cost software-defined network and adaptive infrastructure configuration. We validated the efficiency of our approach on a real-world computing continuum infrastructure. The evaluation results confirm that our flexible approach is suitable for all but the most bandwidth-intensive applications.
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Poster-Paper accepted: Unique Prefix vs. Unique Mask for Minimizing SDN Flows with Transparent Edge Access

23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023) Bangalore, India
Authors: Josef Hammer and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)
Abstract: Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network that developers can utilize for their applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking (SDN) and virtual IP+port addresses for registered edge services. In this work, we introduce the Unique Mask, a solution superior to the Unique Prefix presented in our previous work that considerably reduces the number of required flows in the switches. Our evaluations show that both algorithms perform very well, with the Unique Mask capable of reducing the number of flows by up to 98 %.
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Paper accepted: Scalable Transparent Access to 5G Edge Services

7th IEEE International Conference on Fog and Edge Computing (ICFEC 2023) held in conjunction with CCGrid 2023 Bangalore, India
Authors: Josef Hammer and Hermann Hellwagner, Alpen-Adria-Universität Klagenfurt
Abstract: The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. One significant technology is Multi-access Edge Computing (MEC), a central piece of 5G telecommunication systems. MEC provides a cloud computing platform at the edge of the radio access network and is particularly essential to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking (SDN) and virtual IP+port addresses for registered edge services.
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EduDay 2023 a great success

On February 8, 2023, EduDay – organised by the educational lab and students of the HAK 1 Klagenfurt – took place for the first time. Several hundred students were guided through the laboratories and got their first insight into research. CD laboratory ATHENA participated as well and presented background and results from the world of video streaming to the interested participants.

Find more info here.

 

 

 

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Paper accepted: Proactive SLA-aware Application Placement in the Computing Continuum in 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS)

37th IEEE International Parallel & Distributed Processing Symposium (IPDPS) May 15-19, 2023, Florida USA

Authors: Zahra Najafabadi Samani (Alpen-Adria-Universität Klagenfurt, Austria), Narges Mehran (Alpen-Adria-Universität Klagenfurt, Austria), Dragi Kimovski (Alpen-Adria-Universität Klagenfurt, Austria), Radu Prodan (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: The accelerating growth of modern distributed applications with low delivery deadlines leads to a paradigm shift towards the multi-tier computing continuum. However, the geographical dispersion, heterogeneity, and availability of the continuum resources may result in failures and quality of service degradation, significantly negating its advantages and lowering users’ satisfaction. We propose in this paper a proactive application placement PROS method relying on distributed coordination to prevent the quality of service violations through service-level agreements on the computing continuum. PROS employs a sigmoid function with adaptive weights for the different parameters to predict the service level agreement assurance of devices based on their past credentials and current capabilities. We evaluate PROS using two application workloads with different traffic stress levels up to 90 million services on a real testbed with 600 heterogeneous instances deployed over eight geographical locations. The results show that PROS increases the success rate by 7-33%, reduces the response time by 16-38%, and increases the deadline satisfaction rate by 19-42% compared to the two related work methods. A comprehensive simulation study with 1000 devices and a workload of up to 670 million services confirms the scalability of the results.

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Paper accepted: SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

IEEE International Conference on Communications (ICC)

28 May – 01 June 2023– Rome, Italy

Conference Website

Reza Farahani (Alpen-Adria-Universität Klagenfurt),  Abdelhak Bentaleb (Concordia University, Canada), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Mohammad Shojafar (University of Surrey, UK), Radu Prodan (Alpen-Adria-Universität Klagenfurt), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: 5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multimedia Services (MS).

To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an SFC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in large-scale scenarios. Finally, we design a large-scale cloud-based testbed, including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users’ QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.

Index Terms—HAS; DASH; NFV; SFC; SDN, Edge Computing.