Athors: Alexander Lercher, Nishant Saurabh, Radu Prodan

The 15th IEEE International Conference on Social Computing and Networking
http://www.swinflow.org/confs/2022/socialcom/

Abstract: Community evolution prediction enables business-driven social networks to detect customer groups modeled as communities based on similar interests by splitting them into temporal segments and utilizing ML classification to predict their structural changes. Unfortunately, existing methods overlook business contexts and focus on analyzing customer activities, raising privacy concerns. This paper proposes a novel method for community evolution prediction that applies a context-aware approach to identify future changes in community structures through three complementary features. Firstly, it models business events as transactions, splits them into explicit contexts, and detects contextualized communities for multiple time windows. Secondly, it %it performs feature engineering by uses novel structural metrics representing temporal features of contextualized communities. Thirdly, it uses extracted features to train ML classifiers and predict the community evolution in the same context and other dependent contexts. Experimental results on two real-world data sets reveal that traditional ML classifiers using the context-aware approach can predict community evolution with up to three times higher accuracy, precision, recall, and F1-score than other baseline classification methods (i.e., majority class, persistence).

A pre-kickoff meeting for Graph-Massivizer took place on November 4, 2022, at Vrije Universiteit Amsterdam. Ana Lucia Varbanescu, Michael Cochez, Alexandru Iosup, and Radu Prodan discussed the scientific goals of the Graph Massivizer project and how to exceed them.

A new online article about Graphmassivizer has been published in the Science blog; check it out HERE.

2022 IEEE/ACM 2nd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC) In conjuction with IEEE/ACM UCC 2022 December 6-9, 2022 | Vancouver, Washington, USA

Authors: Narges Mehran (Alpen-Adria-Universität Klagenfurt) and Radu Prodan (Alpen-Adria-Universität Klagenfurt)

Abstract: Processing rapidly growing data encompasses complex workflows that utilize the Cloud for high-performance computing and the Fog and Edge devices for low-latency communication. For example, autonomous driving applications require inspection, recognition, and classification of road signs for safety inspection assessments, especially on crowded roads. Such applications are among the famous research and industrial exploration topics in computer vision and machine learning. In this work, we design a road sign inspection workflow consisting of 1) encoding and framing tasks of video streams captured by camera sensors embedded in the vehicles, and 2) convolutional neural network (CNN) training and inference models for accurate visual object recognition. We explore a matching theoretic algorithm named CODA [1] to place the workflow on the computing continuum, targeting the workflow processing time, data transfer intensity, and energy consumption as objectives. Evaluation results on a real computing continuum testbed federated among four Cloud, Fog, and Edge providers reveal that CODA achieves 50%-60% lower completion time, 33%-59% lower CO2 emissions, and 19%-45% lower data transfer intensity compared to two stateof-the-art methods.

29th International Conference on MultiMedia Modeling
9 – 12 January 2023 | Bergen, Norway

Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract:

HTTP Adaptive Streaming (HAS) is the predominant technique to deliver video contents across the Internet with the increasing demand of its applications. With the evolution of videos to deliver more immersive experiences, such as their evolution in resolution and framerate, highly efficient video compression schemes are required to ease the burden on the delivery process. While AVC/H.264 still represents the most adopted codec, we are experiencing an increase in the usage of new generation codecs (HEVC/H.265, VP9, AV1, VVC/H.266, etc.). Compared to AVC/H.264, these codecs can either achieve the same quality besides a bitrate reduction or improve the quality while targeting the same bitrate. In this paper, we propose a Mixed-Binary Linear Programming (MBLP) model called Multi-Codec Optimization Model at the edge for Live streaming (MCOM-Live) to jointly optimize (i) the overall streaming costs, and (ii) the visual quality of the content played
out by the end-users by efficiently enabling multi-codec content delivery. Given a video content encoded with multiple codecs according to a fixed bitrate ladder, the model will choose among three available policies, i.e., fetch, transcode, or skip, the best option to handle the representations. We compare the proposed model with traditional approaches used in the industry. The experimental results show that our proposed method can reduce the additional latency by up to 23% and the streaming costs by up to 78%, besides improving the visual quality of the delivered segments by up to 0.5 dB, in terms of PSNR.

MCOM architecture overview.

On 20 October 2022 Felix gave the talk “Walk Like an Englishman? The Cultural Experience of Walking Simulators” as part of the Transformative Play Initiative 2022 conference at Uppsala University, Sweden.
The content of the presentation was accepted for an article in the International Journal of Role-Playing (IJRP).
Felix talk titled “A Walk in the Park? Designing a Very British Gaming Experience” which he will be given together with Christoph Kaindel has been accepted for the Future and Reality of Gaming (FROG) Conference 2022 at Danube University Krems.

OTEC: An Optimized Transcoding Task Scheduler for Cloud and Fog Environments

ACM CoNEXT 2022ViSNext

Samira Afzal (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Hamid Hadian (Alpen-Adria-Universität Klagenfurt), Alireza Erfanian (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Radu Prodan (Alpen-Adria-Universität Klagenfurt)

Abstract:

Encoding and transcoding videos into multiple codecs and representations is a significant challenge that requires seconds or even days on high-performance computers depending on many technical characteristics, such as video complexity or encoding parameters. Cloud computing offering on-demand computing resources optimized to meet the needs of customers and their budgets is a promising technology for accelerating dynamic transcoding workloads. In this work, we propose OTEC, a novel multi-objective optimization method based on the mixed-integer linear programming model to optimize the computing instance selection for transcoding processes. OTEC determines the type and number of cloud and fog resource instances for video encoding and transcoding tasks with optimized computation cost and time. We evaluated OTEC on AWS EC2 and Exoscale instances for various administrator priorities, the number of encoded video segments, and segment transcoding times. The results show that OTEC can achieve appropriate resource selections and satisfy the administrator’s priorities in terms of time and cost minimization.

OTEC architecture overview.

As a Hipeac member, we are hosting Zeinab Bakhshi, a Ph.D. student from Mälardalens University in Sweden. Zeinab achieved a Hipeac collaboration grant and is now hosted by Profesor Radu Prodan to expand her research on container-based fog architectures. Taking advantage of the multi-layer continuum computing architecture in Klagenfurt lab helps Zeinab deploy the use case she is researching on. These scientific experiments take her research work to the next level. We are planning to publish our collaborative research work in a series of papers based on the upcoming results.

Vignesh V Menon

Transactions on Multimedia Computing Communications and Applications (TOMM)

Journal Website

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:

In HTTP Adaptive Streaming (HAS), videos are encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to the heterogeneity of network conditions, device attributes, and end-user preferences. Encoding the same video segment at
multiple representations increases costs for content providers. State-of-the-art multi-encoding schemes improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to reduce the encoding time of the remaining
representations. These schemes typically use the highest bitrate representation as the reference to accelerate the encoding of the remaining representations. Nowadays, most streaming services utilize cloud-based encoding techniques, enabling a fully parallel
encoding process to reduce the overall encoding time. The highest bitrate representation has the highest encoding time than the other representations. Thus, utilizing it as the reference encoding is unfavorable in a parallel encoding setup as the overall encoding time is bound by its encoding time. This paper provides a comprehensive study of various multi-rate and multi-encoding schemes in both serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the Rate Distortion Optimization (RDO) process across various representations. Based on these heuristics, three multi-encoding schemes are proposed, which rely on encoder analysis sharing across different representations: (i) optimized for the highest compression efficiency, (ii) optimized for the best compression efficiency-encoding time savings trade-off, and (iii) optimized for the best encoding time savings. Experimental results demonstrate that the proposed multi-encoding schemes (i), (ii), and (iii) reduce the overall serial encoding time by 34.71%, 45.27%, and 68.76% with a 2.3%, 3.1%, and 4.5% bitrate increase to maintain the same VMAF, respectively compared to stand-alone encodings. The overall parallel encoding time is reduced by 22.03%, 20.72%, and 76.82% compared to stand-alone encodings for schemes (i), (ii), and (iii), respectively.

An example of video representations’ storage in HAS. The input video is encoded at multiple resolutions and bitrates. Novel multi-rate and multi-resolution encoder
analysis sharing methods are presented to accelerate encoding in more than one representation.

2022 IEEE International Conference on Image Processing (ICIP)

October 16-19, 2022 | Bordeaux, France

Conference Website

 

Abstract: According to the Bitmovin Video Developer Report 2021, live streaming at scale has the highest scope for innovation in video streaming services. Currently, there are no open-source implementations available which can predict video complexity for live streaming applications. To this light, we plan to demo the functions of VCA software, and show accuracy of the complexities analyzed by VCA (https://vca.itec.aau.at) using the heatmaps, and show-case the speed of video complexity analysis. VCA can achieve an analysis speed of about 370fps compared to the 5fps speed of the reference SITI implementation. Hence, we show that it can be used for live streaming applications.

In the demo, we also showcase an application of VCA in detail: optimized CRF prediction for adaptive streaming, which is being presented in ICIP’22 (Paper ID: 2030). This scheme improves the compression efficiency of the conventional ABR encoding for live streaming.

Contributors:

  • Vignesh V Menon, University of Klagenfurt, Austria (vignesh.menon@aau.at)
  • Christian Feldmann, Bitmovin, Austria (christian.feldmann@bitmovin.com)
  • Hadi Amirpour, University of Klagenfurt, Austria (hadi.amirpour@aau.at)
  • Christian Timmerer, Bitmovin, Austria (christian.timmerer@bitmovin.com)