SOFA 2020: Guest of Honor and Talk
Prof. Radu Prodan ist guest of honor at the 9th International Workshop on Soft Computing Applications (SOFA), 27-29 Nov 2020, Arad, Romania. The title of his talk is “Distribute one Billion”.
Project ADAPT: Newspaper Article in “Kronen Zeitung”
The newspaper “Kronen Zeitung” published the article “IM KAMPF GEGEN CORONA: Universität Klagenfurt forscht mit den Chinesen” with Prof. Radu Prodan.
Cluster Computing paper: FastTTPS: Fast Approach for Video Transcoding Time Prediction and Scheduling for HTTP Adaptive Streaming Videos
Authors: Prateek Agrawal (University of Klagenfurt, Austria), Anatoliy Zabrovskiy (University of Klagenfurt, Austria), Adithyan Ilagovan (Bitmovin Inc., CA, USA), Christian Timmerer (University of Klagenfurt, Austria), Radu Prodan (University of Klagenfurt, Austria)
Abstract: HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called Fast video Transcoding Time Prediction and Scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. Read more
Project ADAPT: newspaper article in “Kleine Zeitung”
The newspaper “Kleine Zeitung” published the article “Medizinische Schutzausrüstung: Neue IT-Lösung soll Menschenleben retten” with Prof. Radu Prodan.
Paper accepted in the 10th IEEE Conference on Big Data and Cloud Computing: “Cloud — Edge Offloading Model for Vehicular Traffic Analysis”
Authors: Dragi Kimovski, Dijana C. Bogatinoska, Narges Mehran, Aleksandar Karadimce, Natasha Paunkoska, Radu Prodan, Ninoslav Marina
Abstract: The proliferation of smart sensing and computing devices, capable of collecting a vast amount of data, has made the gathering of the necessary vehicular traffic data relatively easy. However, the analysis of these big data sets requires computational resources, which are currently provided by the Cloud Data Centers. Nevertheless, the Cloud Data Centers can have unacceptably high latency for vehicular analysis applications with strict time requirements. The recent introduction of the Edge computing paradigm, as an extension of the Cloud services, has partially moved the processing of big data closer to the data sources, thus addressing this issue. Unfortunately, this unlocked multiple challenges related to resources management. Therefore, we present a model for scheduling of vehicular traffic analysis applications with partial task offloading across the Cloud — Edge continuum. The approach represents the traffic applications as a set of interconnected tasks composed into a workflow that can be partially offloaded to the Edge. We evaluated the approach through a simulated Cloud — Edge environment that considers two representative vehicular traffic applications with a focus on video stream analysis. Our results show that the presented approach reduces the application response time up to eight times while improving energy efficiency by a factor of four.
Feedback on online-teaching
Teaching in times of Corona is a particular challenge. An online survey among AAU students shows that they were delighted with the digital teaching formats. Here you will find the best feedback from the students:
https://www.aau.at/feedback-zur-online-lehre/
Josef received very positive reviews, for example: “The course became more and more enjoyable, not only because of the content but also because of the technical aids: He integrated special effects, course intro, applause at the weekly quizzes, which significantly loosened the atmosphere.”
FFG project “ADaptive and Autonomous data Performance connectivity and decentralized Transport decision-making Network” (ADAPT) accepted
This project started during the most critical phase of the COVID-19 outbreak in Europe where the demand for Personal Protective Equipment (PPE) from each country’s health care system has
surpassed national stock amounts by far. Therefore, the ADAPT consortia agreed to bundle its joint resources to develop and adaptive and autonomous decision-making network to support the involved stakeholders along the PPE Supply Chain in their endeavour to save and protect human lives as quickly as possible.
The partners will do that by providing a Blockchain solution capable of optimizing supply, demand and transport capacities between them, elaborating a technical solution for transparent and realtime certification checks on equipment and production documentation as well as distributed and parallel decision-making capabilities on all levels of this multi-dimensional research problem.
In total, the world community will spent more than € 49,6 billion on PPE medical equipment in 2020, € 7,7 billion thereof could be saved with the transport optimization of ADAPT and additional € 5,18 billion could be freed up in the financing and banking sector which could be reinvested immediately into the expansion of the world’s national health care systems.
ADAPT is a 36-month duration project submitted to 6th Call for Austrian-Chinese Coop. RTD Projects FFG & CAS.
Partners:
- Alpen-Adria Universität Klagenfurt, Institute of Information Technology (UNI-KLU)
- Johannes-Kepler-Universität Linz, Intelligent Transport Systems-Sustainable Transport Logistics 4.0. (JKU)
- Logoplan – Logistik, Verkehrs und Umweltschutz Consulting GmbH (LP)
- Intact GmbH (INTACT)
- Chinese Academy of Sciences, Institute of Computing Technology (ICTCAS)
Paper accepted in IEEE Internet Computing: “Inter-host Orchestration Platform Architecture for Ultra-scale Cloud Applications”
The manuscript “Inter-host Orchestration Platform Architecture for Ultra-scale Cloud Applications” has been accepted for publication in an upcoming issue of IEEE Internet Computing.
Authors: Sasko Ristov, Thomas Fahringer, Radu Prodan, Magdalena Kostoska, Marjan Gusev, Shahram Dustdar
Abstract: Cloud data centers exploit many memory page management techniques that reduce the total memory utilization and access time. Mainly these techniques are applied to a hypervisor in a single host (intra-hypervisor) without the possibility to exploit the knowledge obtained by a group of hosts (clusters). We introduce a novel inter-hypervisor orchestration platform to provide intelligent memory page management for horizontal scaling. It will use the performance behavior of faster virtual machines to activate pre-fetching mechanisms that reduce the number of page faults. The overall platform consists of five modules – profiler, collector, classifier, predictor, and pre-fetcher. We developed and deployed a prototype of the platform, which comprises the first three modules. The evaluation shows that data collection is feasible in real-time, which means that if our approach is used on top of the existing memory page management techniques, it can significantly lower the miss rate that initiates page faults.
Paper accepted MMM’21: Towards Optimal Multirate Encoding for HTTP Adaptive Streaming
Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt),Ekrem Çetinkaya (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK)
Abstract: HTTP Adaptive Streaming (HAS) enables high quality stream-ing of video contents. In HAS, videos are divided into short intervalscalled segments, and each segment is encoded at various quality/bitratesto adapt to the available bandwidth. Multiple encodings of the same con-tent imposes high cost for video content providers. To reduce the time-complexity of encoding multiple representations, state-of-the-art methods typically encode the highest quality representation first and reusethe information gathered during its encoding to accelerate the encodingof the remaining representations. As encoding the highest quality rep-resentation requires the highest time-complexity compared to the lowerquality representations, it would be a bottleneck in parallel encoding scenarios and the overall time-complexity will be limited to the time-complexity of the highest quality representation. In this paper and toaddress this problem, we consider all representations from the highestto the lowest quality representation as a potential, single reference toaccelerate the encoding of the other, dependent representations. We for-mulate a set of encoding modes and assess their performance in terms ofBD-Rate and time-complexity, using both VMAF and PSNR as objec-tive metrics. Experimental results show that encoding a middle qualityrepresentation as a reference, can significantly reduce the maximum en-coding complexity and hence it is an efficient way of encoding multiplerepresentations in parallel. Based on this fact, a fast multirate encodingmethod is proposed which utilizes depth and prediction mode of a middle quality representation to accelerate the encoding of the dependentrepresentations.
The International MultiMedia Modeling Conference (MMM)
25-27 January 2021, Prague, Czech Republic
Link: https://mmm2021.cz
Keywords: HEVC, Video Encoding , Multirate Encoding , DASH
Grand Challenge Keynote on “Deep Video Understanding and the User” at ACMMM2020
Today, Klaus Schöffmann will present his keynote talk on “Deep Video Understanding and the User” at the ACM Multimedia 2020 Grand Challenge (GC) on “Deep Video Understanding”. The talk will highlight user aspects of automatic video content search, based on deep neural networks, and show several examples where users have serious issues in finding the correct content scene, when video search systems rely too much on the “automatic search” scenario and ignore the user behind. Registered users of ACMMM2020 can watch the talk online; the corresponding GC is scheduled for October 14 from 21:00-22:00 (15:00-16:00 NY Time).
Link: https://2020.acmmm.org/