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.
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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.
Title: Fast multi-rate encoding for adaptive HTTP streaming
Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), Ekrem Çetynkaya (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)
Abstract: According to embodiments of the disclosure, information of higher and lower quality encoded video segments is used to limit Rate-Distortion Optimization (RDO) for each Coding Unit Tree (CTU). A method first encodes the highest bit-rate segment and consequently uses it to encode the lowest bit-rate video segment. Block structure and selected reference frame of both highest and lowest bit-rate video segments are used to predict and shorten RDO process for each CTU in middle bit-rates. The method delays just one frame using parallel processing. This approach provides time-complexity reduction compared to the reference software for middle bit-rates while degradation is negligible. Read more
Radu Prodan presented the Graph-Massivizer project at the “Get-to-know” introductory and welcome day, part of Data Spaces Support Centre activities on 23 February.