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.

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.

 

Alpen-Adria Universität Klagenfurt, Institute of Information Technology Chinese Academy of Sciences, Institute of Automation Johannes-Kepler-Universität Linz, Intelligent Transport Systems- Sustainable Transport Logistics 4.0 Logoplan – Logistik, Verkehrs und Umweltschutz Consulting GmbH Intact GmbH Chinese Academy of Sciences, Institute of Computing Technology

Title: Boosting the Impact of Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe

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: Nuria de Lama Sanchez (International Data Corporation Madrid, Spain), Peter Haase (metaphacts GmbH Walldorf, Germany), Dumitru Roman (SINTEF AS), and Radu Prodan (University of Klagenfurt
Klagenfurt, Austria)

Abstract: We explore the potential of the Graph-Massivizer project funded by the Horizon Europe research and innovation program of the European Union to boost the impact of extreme and sustainable graph processing for mitigating existing urgent societal challenges. Current graph processing platforms do not support diverse workloads, models, languages, and algebraic frameworks. Existing specialized platforms are difficult to use by non-experts and suffer from limited portability and interoperability, leading to redundant efforts and inefficient resource and energy consumption due to vendor and even platform lock-in. While synthetic data emerged as an invaluable resource overshadowing actual data for developing robust artificial intelligence analytics, graph generation remains a challenge due to extreme dimensionality and complexity. On the European scale, this practice is unsustainable and, thus, threatens the possibility of creating a climate-neutral and sustainable economy based on graph data. Making graph processing sustainable is essential but needs credible evidence. The grand vision of the Graph-Massivizer project is a technological solution, coupled with field experiments and experience-sharing, for a high-performance and sustainable graph processing of extreme data with a proper response for any need and organizational size by 2030.

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.

Title: Towards Sustainable Serverless Processing of Massive Graphs on the Computing Continuum

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: Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Dragi Kimovski (Alpen-Adria-Universität Klagenfurt, Austria), Sashko Ristov (Universität Innsbruck, Austria), Alexandru Iosup (Vrije Universiteit Amsterdam, Netherland), Radu Prodan (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: With the ever-increasing volume of data and the demand to analyze and comprehend it, graph processing has become an essential apapproach for solving complex problems in various domains, like social networks, bioinformatics, and finance. Despite the potential benefits
of current graph processing platforms, they often encounter difficultures supporting diverse workloads, models, and languages. Moreover, existing specialized platforms suffer from limited portability and interoperability, resulting in redundant efforts and inefficient resource and energy utilization due to vendor and even platform lock-in. To bridge the aforementioned gaps, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. In this paper, we briefly introduce the Graph-Massivizer platform. We then leverage the emerging serverless computing paradigm to devise Graph-Serverlizer, a scalable graph analytics tool over a codesigned computing continuum infrastructure. Finally, we sketch six crucial research questions in Graph-Serverlizer’s design and outline three ongoing and future research directions for addressing them.

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.

36th IEEE/IFIP Network Operations and Management Symposium (NOMS 2023) Miami, USA
Authors: Josef Hammer, Dragi Kimovski, Narges Mehran, Radu Prodan, and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)
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.
23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023) Bangalore, India
Authors: Josef Hammer and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)
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 that developers can utilize for their applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking (SDN) and virtual IP+port addresses for registered edge services. In this work, we introduce the Unique Mask, a solution superior to the Unique Prefix presented in our previous work that considerably reduces the number of required flows in the switches. Our evaluations show that both algorithms perform very well, with the Unique Mask capable of reducing the number of flows by up to 98 %.
7th IEEE International Conference on Fog and Edge Computing (ICFEC 2023) held in conjunction with CCGrid 2023 Bangalore, India
Authors: Josef Hammer 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. One significant technology is Multi-access Edge Computing (MEC), a central piece of 5G telecommunication systems. MEC provides a cloud computing platform at the edge of the radio access network and is particularly essential to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking (SDN) and virtual IP+port addresses for registered edge services.
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