Titles: Modern Network-Assisted Delivery of Adaptive Video Streaming Services and Towards Sustainable Servessless Processing of Massive Graphs on Computing Continuum
Link: https://springschool.iaik.tugraz.at/
Multimedia Communication
Josef Hammer presented the paper “C3-Edge – An Automated Mininet-Compatible SDN Testbed On Raspberry Pis and Nvidia Jetsons” at the 36th IEEE/IFIP Network Operations and Management Symposium (NOMS 2023) in Miami, Florida, USA.
Authors: Josef Hammer, Dragi Kimovski, Narges Mehran, Radu Prodan, and Hermann Hellwagner – Alpen-Adria-Universität Klagenfurt
Abstract: The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. While there is vast potential for research in these areas, managing complex adaptive infrastructure is difficult, and experiments with real hardware are tedious to set up. Furthermore, proposed solutions often require expensive hardware or labor-intensive procedures to replicate and build on these ideas. With our C3-Edge testbed, we address these challenges and propose a novel approach for automated edge testbed setup with a low-cost software-defined network and adaptive infrastructure configuration. We validated the efficiency of our approach on a real-world computing continuum infrastructure. The evaluation results confirm that our flexible approach is suitable for all but the most bandwidth-intensive applications.
For more information about the research, visit the website: https://edge.itec.aau.at/.
Josef Hammer received the 2nd Place Outstanding Poster Award at the IPDPS PhD Forum 2023 for his poster titled “Distributed On-Demand Deployment for Transparent Access to 5G Edge Computing Services.” The event took place in St. Petersburg, Florida, USA, and was attended by Josef Hammer and Radu Prodan.
The recognition was part of the 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2023). For more information about the research and its contributors, visit the website: https://edge.itec.aau.at/.
Josef Hammer presented the paper “Distributed On-Demand Deployment for Transparent Access to 5G Edge Computing Services” at the 5th Workshop on Parallel AI and Systems for the Edge (PAISE 2023). The workshop was held in conjunction with the 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2023) in St. Petersburg, Florida, USA.
Authors: Josef Hammer and Hermann Hellwagner, Alpen-Adria-Universität Klagenfurt
Abstract: Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network. Our previous publications argue that edge computing should be transparent to clients, leveraging Software-Defined Networking (SDN). While we introduced a solution to implement such a transparent approach, one question remained: How to handle user requests to a service that is not yet running in a nearby edge cluster? One advantage of the transparent edge is that one could process the initial request in the cloud. However, this paper argues that on-demand deployment might be fast enough for many services, even for the first request. We present an SDN controller that automatically deploys an application container in a nearby edge cluster if no instance is running yet. In the meantime, the user’s request is forwarded to another (nearby) edge cluster or kept waiting to be forwarded immediately to the newly instantiated instance. Our performance evaluations on a real edge/fog testbed show that the waiting time for the initial request – e.g., for an nginx-based service – can be as low as 0.5 seconds – satisfactory for many applications.
For more information about the research, visit the website: https://edge.itec.aau.at/.
2023 ACM Mile High Video (MHV)
May 7-10, 2023 | Denver, US
Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Prajit T Rajendran (Universite Paris-Saclay), Mohammed Ghanbari (University of Essex), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).
Abstract:
In recent years, video streaming applications have proliferated the demand for Video Quality Assessment (VQA). Reduced reference video quality assessment (RR-VQA) is a category of VQA where certain features (e.g., texture, edges) of the original video are provided for quality assessment. It is a popular research area for various applications such as social media, online games, and video streaming. This paper introduces a reduced reference Transcoding Quality Prediction Model (TQPM) to determine the visual quality score of the video possibly transcoded in multiple stages. The quality is predicted using Discrete Cosine Transform (DCT)-energy-based features of the video (i.e., the video’s brightness, spatial texture information, and temporal activity) and the target bitrate representation of each transcoding stage. To do that, the problem is formulated, and a Long Short-Term Memory (LSTM)-based quality prediction model is presented. Experimental results illustrate that, on average, TQPM yields PSNR, SSIM, and VMAF predictions with an ?2 score of 0.83, 0.85, and 0.87, respectively, and Mean Absolute Error (MAE) of 1.31 dB, 1.19 dB, and 3.01, respectively, for single-stage transcoding.
Furthermore, an ?2 score of 0.84, 0.86, and 0.91, respectively, and MAE of 1.32 dB, 1.33 dB, and 3.25, respectively, are observed for a two-stage transcoding scenario. Moreover, the average processing time of TQPM for 4s segments is 0.328s, making it a practical VQA method in online streaming applications.
IEEE Access, A Multidisciplinary, Open-access Journal of the IEEE
[PDF; GitHub]
Babak Taraghi , Hermann Hellwagner and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)
Low-latency live streaming by HTTP Chunked Transfer Encoding
Abstract: Live media streaming is a challenging task by itself, and when it comes to use cases that define low-latency as a must, the complexity will rise multiple times. In a typical media streaming session, the main goal can be declared as providing the highest possible Quality of Experience (QoE), which has proved to be measurable using quality models and various metrics. In a low-latency media streaming session, the requirements are to provide the lowest possible delay between the moment a frame of video is captured and the moment that the captured frame is rendered on the client screen, also known as end-to-end (E2E) latency and maintain the QoE. This paper proposes a sophisticated cloud-based and open-source testbed that facilitates evaluating a low-latency live streaming session as the primary contribution. Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation (LLL-CAdViSE) framework is enabled to asses the live streaming systems running on two major HTTP Adaptive Streaming (HAS) formats, Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS). We use Chunked Transfer Encoding (CTE) to deliver Common Media Application Format (CMAF) chunks to the media players. Our testbed generates the test content (audiovisual streams). Therefore, no test sequence is required, and the encoding parameters (e.g., encoder, bitrate, resolution, latency) are defined separately for each experiment. We have integrated the ITU-T P.1203 quality model inside our testbed. To demonstrate the flexibility and power of LLL-CAdViSE, we have presented a secondary contribution in this paper; we have conducted a set of experiments with different network traces, media players, ABR algorithms, and with various requirements (e.g., E2E latency (typical/reduced/low/ultra-low), diverse bitrate ladders, and catch-up logic) and presented the essential findings and the experimental results.
Keywords: Live Streaming; Low-latency; HTTP Adaptive Streaming; Quality of Experience; Objective Evaluation, Open-source Testbed.
14th ACM Multimedia Systems Conference (MMSys)
7 – 10 June 2023 | Vancouver, BC, Canada
Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt)
Abstract:
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
5th Workshop on Parallel AI and Systems for the Edge (PAISE 2023) held in conjunction with 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2023) St. Petersburg, Florida, USA
Authors: Josef Hammer and Hermann Hellwagner, Alpen-Adria-Universität Klagenfurt
Abstract: Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network. Our previous publications argue that edge computing should be transparent to clients, leveraging Software-Defined Networking (SDN). While we introduced a solution to implement such a transparent approach, one question remained: How to handle user requests to a service that is not yet running in a nearby edge cluster? One advantage of the transparent edge is that one could process the initial request in the cloud. However, this paper argues that on-demand deployment might be fast enough for many services, even for the first request. We present an SDN controller that automatically deploys an application container in a nearby edge cluster if no instance is running yet. In the meantime, the user’s request is forwarded to another (nearby) edge cluster or kept waiting to be forwarded immediately to the newly instantiated instance. Our performance evaluations on a real edge/fog testbed show that the waiting time for the initial request – e.g., for an nginx-based service – can be as low as 0.5 seconds – satisfactory for many applications.
SPACE: Segment Prefetching and Caching at the Edge for Adaptive Video Streaming
Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)
Abstract: Multi-access Edge Computing (MEC) is a new paradigm that brings storage and computing close to the clients. MEC enables the deployment of complex network-assisted mechanisms for video streaming that improve clients’ Quality of Experience (QoE). One of these mechanisms is segment prefetching, which transmits the future video segments in advance closer to the client to serve content with lower latency. In this work, for HAS-based (HTTP Adaptive Streaming) video streaming and specifically considering a cellular (e.g., 5G) network edge, we present our approach Segment Prefetching and Caching at the Edge for Adaptive Video Streaming (SPACE). We propose and analyze different segment prefetching policies that differ in resource utilization, player and radio metrics needed, and deployment complexity. This variety of policies can dynamically adapt to the network’s current conditions and the service provider’s needs. We present segment prefetching policies based on diverse approaches and techniques: past segment requests, segment transrating (i.e., reducing segment bitrate/quality), Markov prediction model, machine learning to predict future segment requests, and super-resolution.We study their performance and feasibility using metrics such as QoE characteristics, computing times, prefetching hits, and link bitrate consumption. We analyze and discuss which segment prefetching policy is better under which circumstances, as well as the influence of the client-side Adaptive Bit Rate (ABR) algorithm and the set of available representations (“bitrate ladder”) in segment prefetching. Moreover, we examine the impact on segment prefetching of different caching policies for (pre-)fetched segments, including Least Recently Used (LRU), Least Frequently Used (LFU), and our proposed popularity-based caching policy Least Popular Used (LPU).
Keywords: Adaptive video streaming, content delivery, HAS, edge computing, cellular network edge, MEC, segment prefetching, segment caching.
Title: Large-scale graph processing and simulation with serverless workflows in federated FaaS
The First Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys), Co-located with ICPE 2023, April 15-19, Coimbra, Portugal (https://sites.google.com/view/graphsys23/home)
Authors: Sashko Ristov (Universität Innsbruck, Austria), Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Radu Prodan (Alpen-Adria-Universität Klagenfurt, Austria)
Abstract: Serverless computing offers a cheap and easy way to code lightweight functions that can be invoked based on some events to perform some simple tasks. For more complicated processing, multiple serverless functions can be orchestrated as a directed acyclic graph and form a serverless workflow or function choreography (FC). While all top cloud providers offer FC systems, as well as there are many open-source FC systems, they are focused on how to describe data flow and control flow between serverless functions of the FC, they rarely consider data that is processed, which often is in the form of a graph. In this paper, we review the support for graph processing of the existing serverless workflow management systerms, detect gaps, and recommend future directions for large-scale graph processing with serverless computing.
