Incremental Multilayer Resource Partitioning for Application Placement in Dynamic Fog

Authors: Zahra Najafabadi Samani, Narges Mehran, Dragi Kimovski, Shajulin Benedikt, Nishant Saurabh, Radu Prodan

IEEE Transactions on Parallel and Distributed Systems

Abstract: Fog computing platforms became essential for deploying low-latency applications at the network’s edge. However, placing and managing time-critical applications over a Fog infrastructure with many heterogeneous and resource-constrained devices over a dynamic network is challenging. This paper proposes an incremental multilayer resource-aware partitioning (M-RAP) method that minimizes resource wastage and maximizes service placement and deadline satisfaction in a dynamic Fog with many application requests. M-RAP represents the heterogeneous Fog resources as a multilayer graph, partitions it based on the network structure and resource types, and constantly updates it upon dynamic changes in the underlying Fog infrastructure. Finally, it identifies the device partitions for placing the application services according to their resource requirements, which must overlap in the same low-latency network partition. We evaluated M-RAP through extensive simulation and two applications executed on a real testbed. The results show that M-RAP can place 1.6 times as many services, satisfy deadlines for 43% more applications, lower their response time by up to 58%, and reduce resource wastage by up to 54% compared to three state-of-the-art methods.

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Congratulations to Priv.-Doz. Dr. Dragi Kimovski: Completion of the Habilitation Procedure

On 03.02.2023 , Dragi Kimovski defended his habilitation thesis “The Computing Continuum in the Internet-of-Things Era: Beyond the Cloud Data Centers”. In the meantime, the procedure has been completed and we were happy to hand out the certificate. Congratulations!

Dragi Kimovski is a tenure track researcher at the Institute of Information Technology (ITEC), University of Klagenfurt. He earned his doctoral degree in 2013 from the Technical University in Sofia. He was an assistant professor at the University of Information Science and Technology in Ohrid and a senior researcher and lecturer at the University of Innsbruck. Kimovski conducted multiple research stays at renowned universities, including the University of Michigan, the University of Utrecht, the University of Bologna, and the University of Granada. He co-authored more than 60 articles in international conferences and journals. His research interests include parallel and distributed computing and multi-objective optimization for energy efficiency and sustainability. He acted as a scientific coordinator and work-package leader in dozen Horizon 2020 projects (DataCloud, ENTICE, and ASPIDE).

Fog and edge computing have been introduced as an extension of the cloud services towards the data sources, thus forming the computing continuum. The computing continuum enables the creation of a new type of services, spanning across distributed infrastructures, supporting various Internet of Things (IoT) applications. However, the introduction of the computing continuum raises multiple challenges for the management, deployment and orchestration of complex distributed applications, such as increased network heterogeneity, limited resource capacity of edge devices, fragmented storage management, high mobility of edge devices and limited support of native monolithic applications. Therefore, the habilitation thesis explores novel algorithms for low latency, scalable, and sustainable computing over heterogeneous resources for information processing and reasoning, thus enabling transparent integration of IoT applications. It tackles the heterogeneity challenge of dynamically changing computing infrastructure topologies and presents a novel concept for sustainable processing at scale.

Vignesh V Menon

Paper accepted: Transcoding Quality Prediction for Adaptive Video Streaming

2023 ACM Mile High Video (MHV) 

May 7-10, 2023 | Denver, US

Conference Website

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.

Presentation of Radu Prodan on “Massive Graphs on the Computing Continuum” in the seminar on “AI meets complex knowledge structures: Neuro-Symbolic AI and Graph Technologies” at the Oslo Metropolitan University.

Presentation of Radu Prodan on “Massive Graphs on the Computing Continuum” in the seminar on “AI meets complex knowledge structures: Neuro-Symbolic AI and Graph Technologies” at the Oslo Metropolitan University.

Paper accepted: LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation framework

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

[PDF; GitHub]

Babak Taraghi , Hermann Hellwagner and Christian Timmerer  (Alpen-Adria-Universität Klagenfurt)

g2g

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.

Paper accepted: QoE- and Energy-aware Content Consumption For HTTP Adaptive Streaming

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.

A Webbing Journey @ Button Festival 2023

On Friday and Saturday (March 10 and March 11, 2023), Sebastian Uitz presented his game “A Webbing Journey” with his partner Michael Steinkellner and Noel Treese at the Button Festival at the Messe Graz. Their booth consisted of 2 PC, a Steam Deck and a Nintendo Switch, all running the game. This was the first time the two handheld devices were used at an event, and people loved playing on them. The Nintendo Switch was a fan favourite for all the kids. The older players tended to the Steam Deck because it’s still a new console, and most of them had never had the chance to play on it before. Similar to other events, a lot of feedback in the form of new ideas for quests and possibilities to extend the game were gathered, which will be implemented in the following weeks and months, ready for the next event in May.

Paper Accepted: Distributed On-Demand Deployment for Transparent Access to 5G Edge Computing Services

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.

Radu Prodan talks at NESTiD Seminar 2022/23

The seminar talks every two weeks are co-organized together with the research group of Networks and Distributed Computing at the University of Liverpool, as part of the Durham-Liverpool synergy. The contact person of this synergy in Liverpool is Leszek Gasieniec.
The seminar talks will be streamed online on zoom. Whenever the speaker is physically present in Durham, the presentation will also be in the Vis-Lab at the 1st floor of the MCS building (in addition to zoom streaming). Please refer to the schedule below for any room changes at some selected talks.

NESTiD Seminar Coordinator: George Mertzios

Title: Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe: The Graph-Massivizer project

Abstract: Graph-Massivizer is a Horizon Europe project that researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph 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 massive graphs. The tools focus on holistic usability (from extreme data ingestion and massive graph 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 massive graph 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 GHG emissions for basic graph operations. Graph-Massivizer gathers an interdisciplinary group of twelve partners from eight countries, covering four academic universities, two applied research centres, one HPC centre, two SMEs and two large enterprises. It leverages the world-leading roles of European researchers in graph processing and serverless computing and uses leadership-class European infrastructure in the computing continuum.

Paper accepted: SPACE: Segment Prefetching and Caching at the Edge for Adaptive Video Streaming

SPACE: Segment Prefetching and Caching at the Edge for Adaptive Video Streaming

IEEE Access

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.