Our paper about LensID – a CNN-RNN-based framework for irregularity detection in cataract surgery videos has been accepted at the prestigious MICCAI 2021 conference (International Conference on Medical Image Computing & Computer Assisted Intervention). Negin Ghamsarian will present the details of her work on this topic in the end of September.
Authors: Negin Ghamsarian, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, and Klaus Schoeffmann
Link: https://www.miccai2021.org/en/
Conference: https://escience2021.org/
Title: Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances
Authors: Roland Mathá, Dragi Kimovski, Anatoliy Zabrovskiy, Christian Timmerer and Radu Prodan
Abstract: Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon’s instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Arm based instances that promise up to 40% better cost performance ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to 33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.
Title: Handover Authentication Latency Reduction using Mobile Edge Computing and Mobility Patterns
Authors: Fatima Abdullah, Dragi Kimovski, Radu Prodan, and Kashif Munir
Abstract: With the advancement in technology and the exponential growth of mobile devices, network traffic has increased manifold in cellular networks. Due to this reason, latency reduction has become a challenging issue for mobile devices. In order to achieve seamless connectivity and minimal disruption during movement, latency reduction is crucial in the handover authentication process. Handover authentication is a process in which the legitimacy of a mobile node is checked when it crosses the boundary of an access network. This paper proposes an efficient technique that utilizes mobility patterns of the mobile node and mobile Edge computing framework to reduce handover authentication latency. The key idea of the proposed technique is to categorize mobile nodes on the basis of their mobility patterns. We perform simulations to measure the networking latency. Besides, we use queuing model to measure the processing time of an authentication query at an Edge servers. The results show that the proposed approach reduces the handover authentication latency up to 54% in comparison with the existing approach.
Link: https://c3.itec.aau.at/index.php/paper-accepted-elsevier-computing/
Our project „ADAPT“ started in March 2021, during the most critical phase of the COVID-19 outbreak in Europe. The demand for Personal Protective Equipment (PPE) from each country’s health care system has surpassed national stock amounts by far.
Learn more about it in an interview with Univ.-Prof. DI Dr. Radu Aurel Prodan in University Klagenfurt´s journal „ad astra“ (pdf).
At IEEE International Conference on Image Processing (ICIP) on September 19-22, 2021, Alaska, USA.
Authors: Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).
Abstract: Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of DASH. The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in VoD HAS applications.
This paper proposes a novel DCT feature-based shot detection and successive elimination algorithm for shot detection algorithm and benchmark the algorithm against the default shot detection algorithm of the x265 implementation of the HEVC standard. Our experimental results demonstrate that the proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.
Keywords: HTTP Adaptive Streaming, Video-on-Demand, Shot detection, multi-shot encoding.
Authors: Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour, (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, University of Essex)
Abstract: Video streaming applications keep getting more attention over the years, and HTTP Adaptive Streaming (HAS) became the de-facto solution for video delivery over the Internet. In HAS, each video is encoded at multiple quality levels and resolutions (i.e., representations) to enable adaptation of the streaming session to viewing and network conditions of the client. This requirement brings encoding challenges along with it, e.g., a video source should be encoded efficiently at multiple bitrates and resolutions. Fast multi-rate encoding approaches aim to address this challenge of encoding multiple representations from a single video by re-using information from already encoded representations. In this paper, a convolutional neural network is used to speed up both multi-rate and multi-resolution encoding for HAS. For multi-rate encoding, the lowest bitrate representation is chosen as the reference. For multi-resolution encoding, the highest bitrate from the lowest resolution representation is chosen as the reference. Pixel values from the target resolution and encoding information from the reference representation are used to predict Coding Tree Unit (CTU) split decisions in High-Efficiency Video Coding (HEVC) for dependent representations. Experimental results show that the proposed method for multi-rate encoding can reduce the overall encoding time by 15.08% and parallel encoding time by 41.26%, with a 0.89% bitrate increase compared to the HEVC reference software. Simultaneously, the proposed method for multi-resolution encoding can reduce the encoding time by 46.27% for the overall encoding and 27.71% for the parallel encoding on average with a 2.05% bitrate
increase.
Keywords: HTTP Adaptive Streaming, HEVC, Multirate Encoding, Machine Learning
The presentation has been accepted to the main-track of the Austrian-Slovenian HPC Meeting (ASHPC’21). Meeting will be organized in a hybrid format on 31 May – 2 June, 2021 at the Institute of Information Science in Maribor, Slovenia.
Title: Automated Workflows Scheduling via Two-Phase Event-based MILP Heuristic for MRCPSP Problem
Authors: Vladislav Kashansky, Gleb Radchenko, Radu Prodan, Anatoliy Zabrovskiy and Prateek Agrawal
Abstract: In today’s reality massive amounts of data-intensive tasks are managed by utilizing a large number of heterogeneous computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable effcient workflows scheduling in such complex environments.
As the scale of the system grows and the workloads become more heterogeneous in the inner structure and the arrival patterns, scheduling problem becomes exponentially harder, requiring problem-specifc heuristics. Many techniques evolved to tackle this problem, including, but not limited to Heterogeneous Earliest Finish Time (HEFT), The Dynamic Scaling Consolidation Scheduling (DSCS), Partitioned Balanced Time Scheduling (PBTS), Deadline Constrained Critical Path (DCCP) and Partition Problem-based Dynamic Provisioning Scheduling (PPDPS). In this talk, we will discuss the two-phase heuristic for makespan-optimized assignment of tasks and computing machines on large-scale computing systems, consisting of matching phase with subsequent event-based MILP method for schedule generation. We evaluated the scalability of the heuristic using the Constraint Integer Programing (SCIP) solver with various configurations based on data sets, provided by the MACS framework. Preliminary results show that the model provides near-optimal assignments and schedules for workflows composed of up to 100 tasks with complex task I/O interactions and demonstrates variable sensitivity with respect to the scale of workflows and resource limitation policies imposed.
Keywords: HPC Schedule Generation, MRCPSP Problem, Workflows Scheduling, Two-Phase Heuristic
Acknowledgement: This work has received funding from the EC-funded project H2020 FETHPC ASPIDE (Agreement #801091)
ADAPT started with the online Kickoff meeting, coordinated by Prof. Radu Prodan.
Prof. Radu Prodan has been nominated as Management Committee (MC) Member CA19135 at COST (European Cooperation in Science & Technologie).
Conference: 15th International Conference on Research Challenges in Information Science
Title : DataCloud: Enabling the Big Data Pipelines on the Computing Continuum
Authors: Dumitru Roman, Nikolay Nikolov, Brian Elvesæter, Ahmet Soylu, Radu Prodan, Dragi Kimovski, Andrea Marrella, Francesco Leotta, Dario Benvenuti, Mihhail Matskin, Giannis Ledakis, Anthony Simonet-Boulogne, Fernando Perales, Evgeny Kharlamov, Alexandre Ulisses, Arnor Solberg and Raffaele Ceccarelli