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Paper accepted: CNN-assisted Road Sign Inspection on the Computing Continuum

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.

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Zeinab Bakhshi, a Ph.D. student from Mälardalens University was at ITEC

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.

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Paper accepted: ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming

IEEE Transactions on Network and Service Management (TNSM)

Journal Website

Authors: Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Shojafar (University of Surry, UK), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: With the ever-increasing demands for high-definition and low-latency video streaming applications, network-assisted video streaming schemes have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context to improve users’ Quality of Experience (QoE) as well as network utilization. Edge computing is considered one of the leading networking paradigms for designing such systems by providing video processing and caching close to the end-users. Despite the wide usage of this technology, designing network-assisted HAS architectures that support low-latency and high-quality video streaming, including edge collaboration is still a challenge. To address these issues, this article leverages the Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing paradigms to propose A collaboRative edge-Assisted framewoRk for HTTP Adaptive video sTreaming (ARARAT). Aiming at minimizing HAS clients’ serving time and network cost, besides considering available resources and all possible serving actions, we design a multi-layer architecture and formulate the problem as a centralized optimization model executed by the SDN controller. However, to cope with the high time complexity of the centralized model, we introduce three heuristic approaches that produce near-optimal solutions through efficient collaboration between the SDN controller and edge servers. Finally, we implement the ARARAT framework, conduct our experiments on a large-scale cloud-based testbed including 250 HAS players, and compare its effectiveness with state-of-the-art systems within comprehensive scenarios. The experimental results illustrate that the proposed ARARAT methods (i) improve users’ QoE by at least 47%, (ii) decrease the streaming cost, including bandwidth and computational costs, by at least 47%, and (iii) enhance network utilization, by at least 48% compared to state-of-the-art approaches.

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Paper accepted: Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe @ IEEE Cloud Summit 2022

IEEE Cloud Summit 2022, https://www.ieeecloudsummit.org/

Authors: Radu Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez,
Alexandru Iosup, Evgeny Kharlamov, Joze Rozanec, Laurentiu Vasiliu, Ana
Lucia Varbanescu

Abstract: The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation based on the emerging serverless computing paradigm supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing.

Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30% improved energy awareness for ETL storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25% lower greenhouse gas emissions for basic graph operations.

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Paper accepted: CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming

18th International Conference on Network and Service Management (CNSM 2022)

Thessaloniki, Greece | 31 October – 4 November 2022

Conference Website

Minh Nguyen (Alpen-Adria-Universität Klagenfurt, Austria), Babak Taraghi (Alpen-Adria-Universität Klagenfurt, Austria), Abdelhak Bentaleb (National University of Singapore, Singapore), Roger Zimmermann (National University of Singapore, Singapore), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Considering network conditions, video content, and viewer device type/screen resolution to construct a bitrate ladder is necessary to deliver the best Quality of Experience (QoE).
A large-screen device like a TV needs a high bitrate with high resolution to provide good visual quality, whereas a small one like a phone requires a low bitrate with low resolution. In
addition, encoding high-quality levels at the server side while the network is unable to deliver them causes unnecessary cost for the content provider. Recently, the Common Media Client Data (CMCD) standard has been proposed, which defines the data that is collected at the client and sent to the server with its HTTP requests. This data is useful in log analysis, quality of service/experience monitoring and delivery improvements.

cadlad

 

In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages CMCD to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on
Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.

 

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Paper accepted: Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach

IEEE Global Communications Conference (GLOBECOM)

December 4-8, 2022 |Rio de Janeiro, Brazil
Conference Website

Authors: Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Abdelhak Bentaleb (National University of Singapore, Singapore), Ekrem Cetinkaya (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Roger Zimmermann (National University of Singapore, Singapore), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: a cost-effective, scalable, and flexible architecture that supports low latency and high-quality live video streaming is still a challenge for Over-The-Top (OTT) service providers. To cope with this issue, this paper leverages Peer-to-Peer (P2P), Content Delivery Network (CDN), edge computing, Network Function Virtualization (NFV), and distributed video transcoding paradigms to introduce a hybRId P2P-CDN arcHiTecture for livE video stReaming (RICHTER). We first introduce RICHTER’s multi-layer architecture and design an action tree that considers all feasible resources provided by peers, edge, and CDN servers for serving peer requests with minimum latency and maximum quality. We then formulate the problem as an optimization model executed at the edge of the network. We present an Online Learning (OL) approach that leverages an unsupervised Self Organizing Map (SOM) to (i) alleviate the time complexity issue of the optimization model and (ii) make it a suitable solution for large-scale scenarios by enabling decisions for groups of requests instead of for single requests. Finally, we implement the RICHTER framework, conduct our experiments on a large-scale cloud-based testbed including 350 HAS players, and compare its effectiveness with baseline systems. The experimental results illustrate that RICHTER outperforms baseline schemes in terms of users’ Quality of Experience (QoE), latency, and network utilization, by at least 59%, 39%, and 70%, respectively.

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STSM-visit of Hamza Baniata @ ITEC

During the period Aug 1st –26th, 2022, Hamza Baniata, a PhD Candidate at the Department of Computer Science, University of Szeged, Hungary, has visited the institute of Information Technology of the University of Klagenfurt, Austria. Under the collaborative supervision by Prof.
Attila Kertesz (SZTE) and Prof. Radu Prodan (ITEC), Hamza has performed several research activities related to the simulation of Blockchain and Fog Computing applications, the enhancement of the FoBSim simulation tool, and the integration of Machine Learning with Blockchain technology. The visit was encouraged and funded by the European COST program under action identifier CA19135 (CERCIRAS), in which Attila, Radu and Hamza are active members. The scientific results of this research visit are currently being edited and finalized in order to be disseminated in an international scientific conference.

Hadi
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IEEE Transactions on Multimedia: Detection and Localization of Video Transcoding From AVC to HEVC Based on Deep Representations of Decoded Frames and PU Maps

Authors: Haichao Yao (Beijing Jiaotong University), Rongrong Ni (Beijing Jiaotong University), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Yao Zhao (Beijing Jiaotong University).

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Paper Accepted @ 25th IEEE Flagship International Conference on Intelligent Transportation Systems

Results of collaborative work in the ADAPT between Austira (FFG) and China (CAS) accteped at flagship conference of IEEE Intelligent Transportation Systems Society

Title: Hybrid On/Off Blockchain Approach for Vehicle Data Management, Processing and Visualization Exemplified by the ADAPT Platform

Authors: Aso Validi, Vladislav Kashansky, Jihed Khiari, Hamid Hadian, Radu Pordan, Juanjuan Li, Fei-Yue Wang, Cristina Olaverri-Monreal

Abstract: Hybrid on/off-blockchain vehicle data management approaches have received a lot of attention in recent years. However, there are various technical challenges remained to deal with. In this paper we relied on real-world data from Austria to investigate the effects of connectivity on the transport of personal protective equipment. We proposed a three-step mechanism to process, simulate, and store/visualize aggregated vehicle datasets together with a formal pipeline process workflow model. To this end, we implemented a hybrid blockchain platform based on the hyperledger fabric and Gluster file systems. The obtained results demonstrated efficiency and stability for both hyperledger fabric and gluster file system, ability of the both on/off-blockchain mechanisms to meet the platform’s quality of service requirements.

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Largest International Video Search Competition Took Place on 11th VBS in Phu Quoc, Vietnam

Over two days (June 6-7, 2022) the 11th Video Browser Showdown (VBS) – www.videobrowsershowdown.org, co-organized by Klaus Schöffmann, took place in Phu Quoc, Vietnam. Sixteen teams participated  in about six hours of competition to solve video content search tasks in a large video dataset (V3C1+V3C2), comprising 2300 hours of video content (17235 video files). Eleven teams on site, together with 5 more teams online, accepted the challenge, competed with each other, and showcased their state-of-the-art video search systems to the conference public of MMM 2022 (http://mmm2022.org).  Congratulations to the winning teams of different search categories: vibro (Germany), CVHunter (Czech Republic), VISIONE (Italy).