Paper accepted: Graph Sampling Quality Prediction for Algorithm Recommendation

Authors: Haleh Dizaji, Reza Farahani, Dragi Kimovski, Joze Rozanec, Ahmet Soylu, Radu Prodan

Venue: 31st IEEE International Conference on High Performance Computing, Data, and Analytics; Bengaluru, India,  18-21 December

https://www.hipc.org

Abstract: The increasing size of graph structures in real-world applications, such as distributed computing networks, social media, or bioinformatics, requires appropriate sampling algorithms that simplify them while preserving key properties. Unfortunately, predicting the outcome of graph sampling algorithms is challenging due to their irregular complexity and randomized properties. Therefore, it is essential to identify appropriate graph features and apply suitable models capable of estimating their sampling outcomes. In this paper, we compare three machine learning (ML) models for predicting the divergence of five metrics produced by twelve node, edge, and traversal-based graph sampling algorithms: degree distribution (D3), clustering coefficient distribution (C2D2), hop-plots distribution (HPD2) (including the largest connected component (HPD2C)), and execution time. We use these prediction models to recommend suitable sampling algorithms for each metric and conduct mutual information analysis to extract relevant graph features. Experiments on six large real-world graphs from three categories (scale-free, power-law, binomial) demonstrate an accuracy under 20% in C2D2 and HPD2 prediction for most algorithms despite the relatively high similarity displacement. Sampling algorithm recommendations on ten real-world graphs show higher hits@3 for D3 and

C2D2 and comparable results for HPD2 and HPD2C compared to the K-best baseline method accessing true empirical data. Finally, ML models show superior runtime recommendations compared to baseline methods, with

hits@3 over 86% for synthetic and real graphs and hits@1 over 60% for small graphs. These findings are promising for algorithm recommendation systems, particularly when balancing quality and runtime preferences.

 

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Reza Farahani gave a tutorial at the 12th European Workshop on Visual Information Processing 2024 in Geneva, Switzerland

Dr. Reza Farahani (University of Klagenfurt, Austria) and Dr. Vignesh V Menon (Fraunhofer HHI, Germany) presented a joint tutorial titled ‘Latency- and Energy-Aware Video Coding and Delivery Streaming Systems’ at the 12th European Workshop on Visual Information Processing (EUVIP 2024) on September 8.

Abstract: This tutorial introduces modern performance and energy-aware video coding and content delivery solutions and tools, focusing on popular video streaming applications, i.e., VoD and live streaming. In this regard, after introducing fundamentals of modern video encoding and networking paradigms, we introduce modern solutions systems, using per-title encoding, per-scene encoding, virtualized and software networks, edge computing, overlay networks such as Content Delivery Networks (CDNs) and/or Peer-to-Peer (P2P) paradigms to provide latency and energy-efficient VoD and live HAS streaming. Furthermore, the tutorial also presents our tools, software, datasets, and testbeds to demonstrate our latest achievements and share practical insights for researchers, engineers, and students who want to improve conversational streaming or even test such techniques for immersive video sequences (e.g., tile-based 360-degree VR) with a focus on latency, economic cost, and energy.

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Paper accepted for 20th International Conference on Network and Service Management (CNSM 2024)

Title: High Complexity and Bad Quality? Efficiency Assessment for Video QoE Prediction Approaches

Authors: Frank Loh, Gülnaziye Bingöl, Reza Farahani, Andrea Pimpinella, Radu Prodan, Luigi Atzori, Tobias Hoßfeld

Venue: 20th International Conference on Network and Service Management (CNSM 2024)

Abstract:  In recent years, video streaming has dominated Internet data traffic, prompting network providers to ensure high-quality streaming experiences to prevent customer churn. However, due to the encryption of streaming traffic, extensive network monitoring by providers is required to predict the streaming quality and improve their services. Several such prediction approaches have been studied in recent years, with a primary focus on the ability to determine key video quality degradation factors, often without considering the required resources or
energy consumption. To address this gap, we consider existing methods to predict key Quality of Experience (QoE) degradation factors from the literature and quantify the data that have to be monitored and processed for video streaming applications. Based on this, we assess the efficiency of different QoE degradation factor prediction approaches and quantify the ratio between efficiency and the achieved prediction quality. In this context, we identify significant disparities in the efficiency, influenced by data requirements and the specific prediction approach, and finally by the resulting quality. Consequently, we provide insights for network providers to choose the most appropriate method tailored to their specific requirements.

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CERCIRAS Summer School @ AAU – 25.-30.08.2024

CERCIRAS (CA19135), short for Connecting Education and Research Communities for an Innovative Resource Aware Society is a 4-year long COST Action, started at the end of September 2020 and now nearing completion. One of the highlights of CERCIRAS has been its yearly Training School, a week-long residential school designed for current and future PhD students with research interests falling within the thematic scope of the Action and open to other profiles, including industry professionals. While perturbed by unfortunate overlap with the outbreak of the COVID pandemic, CERCIRAS managed to execute three consecutive Training Schools successfully: mid-September 2022 in Split (HR); early September 2023 in Riga (LV); and late August 2024 in Klagenfurt (AT). All editions of the CERCIRAS Training School follow a common structure: 4 selected lecture topics, which alternate frontal lessons and assisted hands-on works; 25 to 35 participants from as many participating countries as possible, with rich diversity for provenance, seniority level, and research focus; a rich portfolio of social activities.

The Training School places a considerable burden on the local hosts, which includes securing comfortable and affordable accommodation for a large troop, providing a modern and spacious lecture hall for all lectures and labs, with refreshments for the two daily breaks and nearby canteens for lunch, and arranging exciting options for a whole-afternoon diversion.

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6G Continuum Kick-Off Meeting and Research Collaboration

Dragi Kimovski recently visited Mother Theresa University in Skopje as part of the OeAD 6G Continuum project, which focuses on developing middleware for Artificial Intelligence over 6G networks. This visit is the next step in the collaboration between the involved institutions. During the stay, the kick-off meeting for the 6G Continuum project took place, bringing together researchers from both institutions to discuss the project’s goals and clarify the the research activities in terms of supporting AI traning and inference in the Edge over 6G networks. In addition to the research focus, this visit also helped to strengthen the ongoing partnership between the institutions. They explored new opportunities for collaboration in teaching and research, including student and faculty exchanges through Erasmus and CEEPUS programs.

Interns at ATHENA (Summer 2024)

Between July and August 2024, the ATHENA Christian Doppler Laboratory hosted four interns working on the following topics:

  • Halime Lezi: Image and Video Compression Pipeline
  • Luka Kaiser: VidStream
  • Julius van Dillen: Enhancing Video Quality with Super-Resolution

At the conclusion of their internships, they presented their work and results, receiving official certificates from the university. This collaboration was mutually beneficial for both the researchers at ATHENA and the interns. Their learning process was enhanced by the dedicated guidance they received, which included personalized mentorship, hands-on training, and continuous support. This comprehensive supervision ensured that they not only developed practical skills but also gained a deeper understanding of the research methodologies and technologies used in the video streaming field. We extend our gratitude to all three interns for their genuine interest, productive efforts, and valuable feedback on the laboratory.

Halime Lezi: “I had an awesome time during my four-week internship at ATHENA. My project was about image and video compression, and I learned a lot about how it works. I also got to use Python, which was both fun and challenging. The work environment at ATHENA was really supportive and interesting. My supervisor, Emanuele Artioli, was super helpful and always ready to answer my questions. He made sure I understood both the practical and theoretical parts of my work, which was really cool. It was also great to work with people from different countries. The team was friendly, and we got along well. The work-life balance was good, with a nice mix of work and relaxation. Overall, my time at ATHENA was very educational and enjoyable. The skills and knowledge I gained during this internship will be really useful for my future studies and career. I’m thankful for the opportunity and the support I received. I would highly recommend this internship to anyone looking for a rewarding and fulfilling experience. It’s a great place to learn, grow, and meet new people.”

Luka Kaiser: “I had an amazing four weeks at ATHENA. It was really nice meeting new colleagues and even making new friends. The project I worked on was exactly what interests me, and if I had any questions, my supervisor, Christian, was always there to help me out. So, thank you very much for that. Overall, this experience has been incredibly valuable, and I learned a lot and gained practical skills that I will definitely use in the future. My time here was both productive and enjoyable. I am grateful for the opportunity and would love to stay connected with everyone I’ve met. Thank you once again for everything.”

Julius van Dillen: “My four-week internship at ATHENA was incredible. I focused on improving image and video quality through Super-Resolution techniques. I had the opportunity to work with a variety of tools and technologies, including FFmpeg, Visual Studio Code, Python, several Super-Resolution architectures, and various video quality metrics. I was truly surprised by how much Super-Resolution enhances video and image quality. I really enjoyed working with these technologies and found the entire process fascinating. I am grateful to have had Daniele as my supervisor; his guidance and support made the experience both easier and more enjoyable. During my internship, I gained valuable insights into the research process and the fundamentals of Python programming.”

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Adaptivity in Video Streaming through the Transition Lens

Published in: From Multimedia Communication to the Future Internet: Essays Dedicated to the Retirement of Prof. Dr. Dr. h.c. Ralf Steinmetz

Authors: Amr Rizk (Leibniz Universität Hannover, Germany), Hermann Hellwagner (AAU, Austria), Christian Timmerer (AAU, Austria), and Michael Zink (University of Massachusetts Amherst, MA, USA)

Abstract: Adaptivity is a cornerstone concept in video streaming. Equipped with the concept of Transitions, we review in this paper adaptivity mechanisms known from classical video streaming scenarios. We specifically highlight how these mechanisms emerge in a specific context, such that their performance finally depends on the deployment conditions. Using multiple examples we highlight the strength of the concept of adaptivity at runtime for video streaming.

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Paper accepted at IEEE Access: Energy-Efficient Spatial and Temporal Resolution Selection for Per-Title Encoding

Authors: Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)

Abstract: Video streaming has become an integral part of our digital lives, driving the need for efficient video delivery. With the growing demand for seamless video delivery, adaptive video streaming has emerged as a solution to support users with varying device capabilities and network conditions. Traditional adaptive streaming relies on a predetermined set of bitrate-resolution pairs, known as bitrate ladders, for encoding. However, this “one-size-fits-all” approach is suboptimal when dealing with diverse video content. Consequently, per-title encoding approaches dynamically select the bitrate ladder for each content. However, in an era when carbon dioxide emissions have become a paramount concern, it is crucial to consider energy consumption. Therefore, this paper addresses the pressing issue of increasing energy consumption in video streaming by introducing a novel approach, ESTR, which goes beyond traditional quality-centric resolution selection approaches. Instead, the ESTR considers both video quality and decoding energy consumption to construct an optimal bitrate ladder tailored to the unique characteristics of each video content. To accomplish this, ESTR encodes each video content using a range of spatial and temporal resolutions, each paired with specific bitrates. It then establishes a maximum acceptable quality drop threshold (τ), carefully selecting resolutions that not only preserve video quality above this threshold but also minimize decoding energy consumption. Our experimental results, at a fixed τ of 2 VMAF steps, demonstrate a 32.87% to 41.86% reduction in decoding energy demand for HEVC-encoded videos across various software decoder implementations and operating systems, with a maximum bitrate increase of 2.52%. Furthermore, on a hardware-accelerated client device, a 46.37% energy saving was achieved during video playback at the expense of a 2.52% bitrate increase. Remarkably, these gains in energy efficiency are achieved while maintaining consistent video quality.

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Radu Prodan @ 5th edition of the Data Science International Summer School in Bukarest

From July 20-28, 2024, the 5th Edition of the Data Science International Summer School, managed by Bucharest Business School (BBS @ ASE) and in collaboration with  GATE Institute at Sofia University “St. Kliment Ohridski” and the projects enRichMyData , Graph-Massivizer , UPCAST , INTEND , and InterTwino took place in Predeal, Romania. Radu participated as a speaker and mentor and also presented the Graph-Massivizer project.

Prestigious Speakers:

Dan Nicolae (University of Chicago, USA), Razvan Bunescu (University of North Carolina at Charlotte, USA), Anna Fensel Wageningen University & Research, the Netherlands), Radu Prodan (University of Klagenfurt, Austria), Ioan Toma (Onlim GmbH, Austria), Dumitru Roman (SINTEF / University of Oslo, Norway), Jože Rožanec (Qlector, Slovenia), Daniel Thilo Schroeder (SINTEF, Norway), Gabriel Terejanu, PhD (University of North Carolina at Charlotte, USA), Hui Song (SINTEF, Norway), Viktor Sowinski-Mydlarz (London Metropolitan University, UK and GATE Institute, Bulgaria), Roberto Avogadro (SINTEF, Norway), Nikolay Nikolov (SINTEF AS, Norway).

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Horizon Europe project DATAPACT accepted

The EU has approved the DATAPACT project (Datapact: Compliance by Design of Data/AI Operations and Pipelines) application.The project has a total volume of 9,9 Mio. Euros and 19 partners, including ITEC (Radu Prodan).

DataPACT will develop novel tools and methodologies that enable efficient, compliant, ethical, and sustainable data/AI operations and pipelines. DataPACT will deliver a transformative approach where compliance, ethics, and environmental sustainability are not afterthoughts but foundational elements of data/AI operations and pipelines.