Distributed and Parallel Systems

On Saturday, during the ICPE2023, the Graph-Massivizer Project organized the GraphSys first workshop on Serverless, ExtremeScale, and Sustainable GraphProcessing Systems

On Saturday, during the #ICPE2023, the Graph-Massivizer Project organized the #GraphSys first #workshop on #Serverless, #ExtremeScale, and #Sustainable #GraphProcessing #Systems

It was great to see so many passionate #attendees eager to share and learn about the latest #advancements in #graphsystems ?️ We had some amazing speakers who shared their #insights and #expertise on the topic with a lot of engaging and thought-provoking discussions ⚡ ??Thanks to everyone who participated and made it such a memorable event!

Paper accepted: Performance Improvement Strategies of Edge-Enabled Social Impact Applications

Title: Performance Improvement Strategies of Edge-Enabled Social Impact Applications

Authors: Shajulin Benedict, S. Vivek Reddy, Bhagyalakshmi M., Jiby Mariya Jose, Radu Prodan

International Conference on Inventive Computation Technologies (ICICT 2023)

Abstract: In recent years, social relationships have been rooted in a blend with technological advancements to eradicate emerging challenges, such as loneliness, poverty, pollution, climate change, health issues, and so forth. IoT-enabled social good applications, accordingly, have emerged in various dimensions. In fact, those developing IoT-enabled social good applications have to diligently consider the efficiency of underlying computational infrastructures. This article explores the performance improvement (PI) aspects of edge intelligence techniques that apply to social good applications. It highlights the most commonly practiced PI methods in the literature. Additionally, the article lists the near-future research perspectives of edge-enabled solutions. The article
will be beneficial to several researchers/practitioners who prefer to address social causes using edge-enabled efficient intelligent techniques.

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.

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.

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.

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Final ADAPT project meeting, funded by the Austrian Research Promotion Agency (FFG) and the Chinese Academy of Sciences.

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

Paper accepted: Boosting the Impact of Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe @ ICPE

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.

23 February. Radu Prodan presented the Graph-Massivizer project at the “Get-to-know” introductory and welcome day, part of Data Spaces Support Centre activities.

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.

HE Data-DSSC-23022023-Agenda

Radu Prodan presented the Graph-Massivizer project at the CERCIRAS COST action in Gdansk, Poland

Radu Prodan presented the Graph-Massivizer project at the CERCIRAS COST action on 7th February in Gdansk, Poland