Distributed and Parallel Systems

Paper accepted: MOGPlay: A Decentralized Crowd Journalism Application for Democratic News Production @ 2022 IEEE/ACM International Conference

Title: MOGPlay: A Decentralized Crowd Journalism Application for Democratic News Production

Authors: Ines Rito Lima, Claudia Marinho,Vasco Filipe, Alexandre Ulisses, Nishant Saurabh, Antorweep Chakravorty, Zhiming Zhao, Atanas Hristov, Radu Prodan

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Abstract: Media production and consumption behaviors are changing in response to  new technologies and demands, giving birth to a new generation of social  applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps). Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audio-visual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audio-visual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate three pilot crowd journalism live scenarios that validate the prototype.

/by
,

Paper accepted: CardioHPC: Serverless Approaches for Real-Time Heart Monitoring of Thousands of Patients @17th Workshop on Workflows in Support of Large-Scale Science

Titel: CardioHPC: Serverless Approaches for Real-Time Heart Monitoring of Thousands of Patients

Authors: Marjan Gusev, Sashko Ristov, Andrei Amza, Armin Hohenegger, Radu Prodan, Dimitar Mileski, Pano Gushev, Goran Temelkov

17th Workshop on Workflows in Support of Large-Scale Science

Abstract: We analyze a heart monitoring center for patients wearing electrocardiogram sensors outside hospitals. This prevents serious heart damages and increases life expectancy and health-care efficiency. In this paper, we address a problem to provide a scalable infrastructure for the real-time processing scenario for at least 10000 patients simultaneously, and efficient fast processing architecture for the postponed scenario when patients upload data after realized measurements. CardioHPC is a project to realize a simulation of these two scenarios using digital signal processing algorithms and artificial intelligence-based detection and classification software for automated reporting and alerting. We elaborate the challenges we met in experimenting with different serverless implementations: 1) container-based on Google Cloud Run, and 2) Function-as-a-Service (FaaS) on AWS Lambda. Experimental results present the effect of overhead in the request and transfer time, and speedup achieved by analyzing the response time and throughput on both container-based and FaaS implementations as serverless workflows.

/by
,

Paper accepted: SimLess: Simulate Serverless Workflows and Their Twins and Siblings in Federated FaaS @2022 ACM Symposium on Cloud Computing

Titel: SimLess: Simulate Serverless Workflows and Their Twins and Siblings in Federated FaaS

Authors: Sashko Ristov, Mika Hautz, Christian Hollaus, Radu Prodan

2022 ACM Symposium on Cloud Computing

Abstract: Many researchers migrate scientific serverless workflows or function choreographies (FC) on Function-as-a-Service (FaaS) to benefit from its high scalability and elasticity. Unfortunately, the heterogeneous nature of federated FaaS hampers decisions on the most appropriate configuration setup to run FCs. Consequently, scientists must choose between accurate but tedious and expensive experiments or simple but cheap but less accurate simulations. Unfortunately, related work mainly supports either simulation models for serverfull workflow applications that run on virtual machines and containers or partial FaaS models for individual serverless functions that focus on execution time and neglect various kinds of federated FaaS overheads. Therefore, this paper introduces SimLess, an FC simulation framework across multiple FaaS providers to achieve accurate FC simulations with a simple and cheap parameter setup. Unlike the costly approaches that use machine learning over time series to predict the behavior of FCs, SimLess introduces two light concepts: (1) twins, representing the same code deployed with the same computing, communication, and storage resources, but in other cloud regions of the same FaaS provider, and (2) siblings, representing the same code deployed in the same region with different computing resources. The novel SimLess simulation model splits the round trip time of a function into several parameters reused among twins and siblings without running them. We evaluated SimLess with two scientific FCs deployed across 18 AWS, Google, and IBM regions. SimLess simulates the cumulative overhead with an average inaccuracy of 8.9% without significant differences between regions for learning and validation. Moreover, SimLess generates an inaccuracy of up to 9.75% for a low concurrency FC executed on a single region, with high concurrency of 2500 functions executed in other regions. Finally, SimLess reduces the parameter setup cost by 77.23% compared to the existing simulation approaches.

/by
, ,

Narges Mehran got the student award to present the paper with the title of “Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum” at IEEE Cluster 2022.



Student travel award at IEEE Cluster 2022

Narges Mehran got the student award for presenting the paper titled “Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum” at IEEE Cluster 2022.

The presentation was on the 7th of September: https://clustercomp.org/2022/program/

/by
,

Paper accepted: Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe @ IEEE Cloud Summit 2022

IEEE Cloud Summit 2022, https://www.ieeecloudsummit.org/

Authors: Radu Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez,
Alexandru Iosup, Evgeny Kharlamov, Joze Rozanec, Laurentiu Vasiliu, Ana
Lucia Varbanescu

Abstract: The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) 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 MGs. The tools focus on holistic usability (from extreme data ingestion and MG 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 MG 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 greenhouse gas emissions for basic graph operations.

/by
,

Radu Prodan, Sashko Ristov and Andrei Amza participated in the CardioHPC project meeting

From August 16.-19.2022, a CardioHPC project meeting took place in Skopje, Macedonia. Radu Prodan, Andrei Amza and Sahsko Ristvo participated for AAU.

/by
,

STSM-visit of Hamza Baniata @ ITEC

During the period Aug 1st –26th, 2022, Hamza Baniata, a PhD Candidate at the Department of Computer Science, University of Szeged, Hungary, has visited the institute of Information Technology of the University of Klagenfurt, Austria. Under the collaborative supervision by Prof.
Attila Kertesz (SZTE) and Prof. Radu Prodan (ITEC), Hamza has performed several research activities related to the simulation of Blockchain and Fog Computing applications, the enhancement of the FoBSim simulation tool, and the integration of Machine Learning with Blockchain technology. The visit was encouraged and funded by the European COST program under action identifier CA19135 (CERCIRAS), in which Attila, Radu and Hamza are active members. The scientific results of this research visit are currently being edited and finalized in order to be disseminated in an international scientific conference.

/by
,

Coordination @ Horizon Europe “Massive Graph Processing of Extreme Data for a Sustainable Economy, Society, and Environment” (Graph-Massivizer) project accepted

Project lead/coordination: Radu Prodan
Project partners: IDC Italia, Peracton Limited, Institut Jozef Stefan, Sintef, Universiteit Twente, metaphacts GmbH, Vrije Universiteit Amsterdam, Cineca, Event Registry, Università di Bologna, Robert Bosch GmbH

Abstract: Graph-Massivizer 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 world-leading roles of European researchers in graph processing and serverless computing and uses leadership-class European infrastructure in the computing continuum.

/by

PHD candidate Kevin Theuermann defended his theses on 12th of July 2022: Trustworthy Service Composition Systems for E-Government

Kevin Theuermann (TU Graz) defended his theses on July 12, 2022.
Title: Trustworthy Service Composition Systems for E-Government
Prof. Radu Prodan was integrated as an external expert to survey the thesis and discuss it with the candidate as part of the defense.

/by
,

Paper Accepted @ Journal of Ambient Intelligence and Humanized Computing

Title: MCred: Multi-Modal Message Credibility for Fake News Detection using BERT and CNN

Journal: Journal of Ambient Intelligence and Humanized Computing

Authors: Pawan Kumar Verma, Prateek Agrawal, Vishu Madaan, Radu Prodan

Abstract:

Online social media enables low cost, easy access, rapid propagation, and easy communication of information, including spreading low-quality fake news. Fake news has become a huge threat to every sector in society, and resulting in decrements in the trust quotient for media and leading the audience into bewilderment. In this paper, we proposed a new framework called Message Credibility (MCred) for fake news detection that utilizes the benefits of local and global text semantics. This framework is the fusion of Bidirectional Encoder Representations from Transformers (BERT) using the relationship between words in sentences for global text semantics and Convolutional Neural Networks (CNN) using N-gram features for local text semantics. We demonstrate through experimental results a popular Kaggle dataset that MCred improves the accuracy over a state-of-the-art model by 1.10%, thanks to its combination of local and global text semantics.

/by