IEEE Access, A Multidisciplinary, Open-access Journal of the IEEE

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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.

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

https://edge.itec.aau.at/

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.

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.

How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions

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Abstract: Empowered by today’s rich tools for media generation and collaborative production and convenient network access to the Internet, video streaming has become very popular. Dynamic adaptive video streaming is a technique used to deliver video content to users over the Internet, where the quality of the video adapts in real time based on the network conditions and the capabilities of the user’s device. HTTP Adaptive Streaming (HAS) has become the de-facto standard to provide a smooth and uninterrupted viewing experience, especially when network conditions frequently change. Improving the QoE of users concerning various applications‘ requirements presents several challenges, such as network variability, limited resources, and device heterogeneity. For example, the available network bandwidth can vary over time, leading to frequent changes in the video quality. In addition, different users have different preferences and viewing habits, which can further complicate live streaming optimization. Researchers and engineers have developed various approaches to optimize dynamic adaptive streaming, such as QoE-driven adaptation, machine learning-based approaches, and multi-objective optimization, to address these challenges. In this talk, we will give an introduction to the topic of video streaming and point out the significant challenges in the field. We will present a layered architecture for video streaming and then discuss a selection of approaches from our research addressing these challenges. For instance, we will present approaches to improve the  QoE of clients in User-generated content applications in centralized and distributed fashions. Moreover, we will present a novel architecture for low-latency live streaming that is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches.