Paper accepted: Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning

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IEEE Open Journal of Signal Processing

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

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Presentation accepted for ASHPC’21

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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)

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ADAPT: online kickoff meeting

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ADAPT started with the online Kickoff meeting, coordinated by Prof. Radu Prodan.

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Prof. Radu Prodan

Nomitation as MC Member at COST

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Prof. Radu Prodan has been nominated as Management Committee (MC) Member CA19135 at COST (European Cooperation in Science & Technologie).

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Prof. Radu Prodan

Paper accepted in RCIS 2021

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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

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Prof. Radu Prodan

Memphis DATA 2021: Keynote Speaker

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Prof. Radu Prodan is a keynote speaker at Memphis DATA 2021, 25th-26th March 2021.

Talk Abstract: We live in a digital world estimated to host around 4 billion Internet users and 10 billion of mobile connections generating 2.5 billion billion of data every day. Managing and extracting value from this sheer amount of raw data requires deep software analysis tools on massive distributed and parallel computing infrastructures aggregating billions of cores and threads. The talk gives an overview of the research activities at the University of Klagenfurt, Austria, on optimising system software support for extreme-scale data processing applications, with focus on scientific simulations, social media and massively multiplayer online games.

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Paper accepted in IEEE Transactions on Computational Social Systems Journal

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Title: WELFake: Word Embedding over Linguistic Features for Fake News Detection

Authors: Pawan Kumar Verma (Lovely Professional University, India | GLA University, India), Prateek Agrawal (University of Klagenfurt, Austria | Lovely Professional University, India), Ivone Amorin (MOG Technologies | University of Porto, Portugal), Radu Prodan (University of Klagenfurt, Austria)

Abstract: Social media is a popular medium for dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disadvantage comes in the form of fake news, as people usually read and share information without caring about its genuineness. Therefore, it is imperative to research methods for the authentication of news. To address this issue, this paper proposes a two phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine learning classification. The first phase pre-processes the dataset and validates the veracity of news content by using linguistic features. The second phase merges the linguistic feature sets with WE and applies voting classification. To validate its approach, this paper also carefully designs a novel WELFake dataset with approximately 72,000 articles, which incorporates different datasets to generate an unbiased classification output. Experimental results show that the WELFake model categorises the news in real and fake with a 96.73% which improves the overall accuracy by 1.31% compared to BERT and 4.25% compared to CNN models. Our frequency-based and focused analyzing writing patterns model outperforms predictive-based related works implemented using the Word2vec WE method by up to 1.73%.

Acknowledgement: ARTICONF project

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The paper “Monte-Carlo Approach to the Computational Capacities Analysis of the Computing Continuum” has been accepted to the main-track of the International Conference on Computational Science (ICCS’2021)

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The full paper has been accepted to the main-track of the International Conference on Computational Science (ICCS’21). Conference will be organized in a virtual format on 16-18 June, 2021.

Title: Monte-Carlo Approach to the Computational Capacities Analysis of the Computing Continuum

Authors: Vladislav Kashansky, Gleb Radchenko, Radu Prodan

Abstract: This article proposes an approach to the problem of computational capacities analysis of the computing continuum via theoretical framework of equilibrium phase-transitions and numerical simulations. We introduce the concept of phase transitions in computing continuum and show how this phenomena can be explored in the context of workflow makespan, which we treat as an order parameter. We simulate the behavior of the computational network in the equilibrium regime within the framework of the XY-model defined over complex agent network with Barabasi-Albert topology. More specifically, we define Hamiltonian over complex network topology and sample the resulting spin-orientation distribution with the Metropolis-Hastings technique. The key aspect of the paper is derivation of the bandwidth matrix, as the emergent effect of the “low-level” collective spin interaction. This allows us to study the first order approximation to the makespan of the “high-level” system-wide workflow model in the presence of data-flow anisotropy and phase transitions of the bandwidth matrix controlled by the means of “noise regime” parameter. For this purpose, we have built a simulation engine in Python 3.6. Simulation results confirm existence of the phase transition, revealing complex transformations in the computational abilities of the agents. Notable feature is that bandwidth distribution undergoes a critical transition from single to multi-mode case. Our simulations generally open new perspectives for reproducible comparative performance analysis of the novel and classic scheduling algorithms.

Keywords: Complex Networks, Computing Continuum, Phase Transitions, Computational Model, MCMC, Metropolis-Hastings, XY-model, Equilibrium Model

Acknowledgement: This work has received funding from the EC-funded project H2020 FETHPC ASPIDE (Agreement #801091)

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The paper “PSTR: Per-title encoding using Spatio-Temporal Resolutions” has been accepted for publication at the IEEE International Conference on Multimedia and Expo (ICME) 2021

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The paper “PSTR: Per-title encoding using Spatio-Temporal Resolutions” has been accepted for publication at the IEEE International Conference on Multimedia and Expo (ICME) 2021 at July 5-9, 2021 Shenzhen, China.

Authors: Hadi Amirpour (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: Current per-title encoding schemes encode the same video content (or snippets/subsets thereof) at various bitrates and spatial resolutions to find an optimal bitrate ladder for each video content. Compared to traditional approaches, in which a predefined, content-agnostic (“fit-to-all”) encoding ladder is applied to all video contents, per-title encoding can result in (i) a significant decrease of storage and delivery costs and (ii) an increase in the Quality of Experience. In the current per-title encoding schemes, the bitrate ladder is optimized using only spatial resolutions, while we argue that with the emergence of high framerate videos, this principle can be extended to temporal resolutions as well. In this paper, we improve the per-title encoding for each content using spatio-temporal resolutions. Experimental results show that our proposed approach doubles the performance of bitrate saving by considering both temporal and spatial resolutions compared to considering only spatial resolutions.

Keywords: Bitrate ladder, per-title encoding, framerate, spatial resolution.

IEEE International Conference on Multimedia and Expo (ICME) , 5-9 July 2021, Shenzhen, China

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OSCAR: On Optimizing Resource Utilization in Live Video Streaming

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Authors: Alireza Erfanian (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Anatoliy Zabrovskiy (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Live video streaming traffic and related applications have experienced significant growth in recent years. However, this has been accompanied by some challenging issues, especially in terms of resource utilization. Although IP multicasting can be recognized as an efficient mechanism to cope with these challenges, it suffers from many problems. Applying software-defined networking (SDN) and network function virtualization (NFV) technologies enable researchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN concept, we introduce OSCAR (Optimizing reSourCe utilizAtion in live video stReaming) as a new cost-aware video streaming approach to provide advanced video coding (AVC)-based live streaming services in the network. In this paper, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder function (VTF). At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller.  Then, by executing a mixed-integer linear program (MILP), the SDN controller determines a group of optimal multicast trees for streaming the requested videos from an appropriate origin server to the VRPs. Moreover, to elevate the efficiency of resource allocation and meet the given end-to-end latency threshold, OSCAR delivers only the highest requested quality from the origin server to an optimal group of VTFs over a multicast tree. The selected VTFs then transcode the received video segments and transmit them to the requesting VRPs in a multicast fashion. To mitigate the time complexity of the proposed MILP model, we present a simple and efficient heuristic algorithm that determines a near-optimal solution in polynomial time. Using the MiniNet emulator, we evaluate the performance of OSCAR in various scenarios. The results show that OSCAR surpasses other SVC- and AVC-based multicast and unicast approaches in terms of cost and resource utilization.

Link: IEEE Transactions on Network and Service Management (TNSM)

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Live Video Streaming, Software Defined Networking (SDN), Video Transcoding, Network Function Virtualization (NFV).

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