Distributed systems in the Post-Moore era

Tuesday, March 14, 2023 | 02:00 pm (CET) | Room: HS 8 | | Alpen-Adria-Universität Klagenfurt

Dr. Vincenzo De Maio | Vienna University of Technology

Abstract: In recent years, we have experienced an exponential growth in the amount of data generated by IoT devices. Data have to be processed strict low latency constraints, that cannot be addressed by conventional computing paradigm and architectures. On top of this, if we consider that we recently hit the limit codified by the Moore’s law, satisfying low-latency requirements of modern applications will become even more challenging in the future. In this talk, we discuss challenges and possibilities of heterogeneous distributed systems in the Post-Moore era.

Bio: Dr. Vincenzo De Maio has a Ph.D. in Computer science by the Institute of Computer Science at the University of Innsbruck, (Austria). He received his Ph.D in November 2016 under the supervision of Univ. Prof. Dr. Radu Prodan and Dr. Gabor Kecskemeti.  

Since 2017 he is working as a postdoctoral researcher at the Institute of Information Systems Engineering of the Vienna University of Technology, whose leader is Univ. Prof. Dr. Ivona Brandic. He has been author of different conference and journal publications on the topic of energy efficiency and modelling for Cloud and Edge computing. His research in the area of parallel and distributed systems includes energy-aware Cloud/Edge computing and Post-Moore computing.

Since 2023, he also collaborates with the Department of Experimental Physics of the University of Innsbruck and Dr. Thomas Monz as CO-PI of the HPQC FFG Leitprojekt Project, whose goal is the integration of quantum hardware in HPC systems.

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Conceptualizations of Software and Data Quality

January 30, 2023 | 02:00 pm (CET) | Room: S.0.05 | | Alpen-Adria-Universität Klagenfurt

Dr.Phil. Volodymyr Shekhovtsov | Department of Informatics Systems

Abstract: Conceptual modeling activities need to integrate software and data quality at the same level of abstraction as the functionality of the system or the data itself (a conceptualization of quality). This need is related to the fact that looking at the problem domain to obtain its conceptual model only from the view of the system’s functionality or the structure of the associated data restricts the analyst and can be the source of mistakes. The reason for such mistakes is that many quality-related issues become only evident when the system is put into use; on the other hand, such mistakes are parts of the early design decisions and can be difficult and costly to fix. This thesis summarizes several selected publications of a research program towards elaborating conceptualizations of software and data quality on different stages of the software development process and for different domains. This program addresses five research challenges. The first challenge is related to the need for the unified classification of the quality conceptualization techniques based on the agreed-upon concept of the quality itself. Without such a classification it is difficult for analysts to decide which quality conceptualization solution is better suited for their problem. This is addressed in the thesis by proposing the classification of such techniques, which can serve as a foundation for the evaluation framework for quality conceptualizations; the rest of the thesis is aligned with this classification. The second challenge is positioned at the intersection of the research fields of static code analysis and requirement engineering. It is related to the problem that the rationale of applying code-checking rules in static code analysis is not captured explicitly, which leads to the problems of rule reuse in similar development contexts. In this thesis, it is proposed to trace possible sources of such rules back to design decisions and quality requirements and to conceptualize the quality[1]related rationale information along with specific code-checking rules to serve as a schema for a rule repository. The third challenge is positioned in the research field of quality-aware requirements engineering. It is related to the problem that more attention should be paid to conceptualizing software quality requirements before performing design-time activities. It is addressed in the thesis by the quality requirements conceptualization activity positioned between the activities of quality requirements elicitation and conceptual design. The results of this activity can be used at different stages of the software process, capturing the quality requirements semantics in a way that can be easily understood and verified by the system users and can be mapped into different design notations. The fourth challenge is positioned in the research field of software process improvement. It is related to the problem that organizing quality-related interaction between business stakeholders and software developers is difficult as they need a common set of concepts. In this thesis, this challenge is addressed by three approaches. First, the thesis proposes to conceptualize the stakeholder interaction process on two levels: a coarse-grained level defining the set of generic quality-related activities and the conditions of launching these activities and a fine-grained level describing specific interaction steps in detail. Second, the thesis proposes to conceptualize such a process as a process of quality view harmonization on three levels: terminology harmonization (agreeing on the common quality-related terminology), view harmonization (agreeing on the sets of objects and the types of their qualities to be assessed, as well as the assessment procedures), and quality harmonization (agreeing on the evaluation schemes and the specific qualities to be used). Third, the thesis introduces a model-centered software solution that facilitates quality-related stakeholder communication in the software development process based on these concepts. The fifth challenge is positioned in the research field of data modeling and management for a specific domain (biobanking). It is related to the problem that the quality of data and metadata in biobanks is not properly defined on the conceptual level and that such conceptualizations are not used to facilitate the biobank search. In this thesis, it is again addressed by three approaches. First, the thesis conceptualizes the data item quality in biobanks and defines a set of data item quality characteristics and metrics. Second, the thesis proposes a definition and a conceptualization for the metadata in biobanks, which covers stored data item quality values, and defines a set of metadata quality characteristics and metrics. Third, the thesis proposes an architecture to support researchers in identifying relevant collections of material and data with documented quality for their research projects while observing strict privacy rules. It is based on the conceptualization of the biobank metadata (including its data item quality values) to serve as a schema for a metadata repository to be queried in a search for collections.

Bio: Volodymyr Shekhovtsov is a postdoctoral researcher at the Department of Informatics Systems (ISYS), University of Klagenfurt. He received his PhD degree in Computerized Control Systems and Progressive Information Technology in 1998 from the National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine. As a researcher, since 2012 he participated in total in 5 research projects at the University of Klagenfurt.

He co-authored more than 45 papers, published in journals such as Applied Sciences, Internet of Things, International Journal of Web Information Systems, and gave 27 talks at venues such as ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS), International Working Conference on Requirements Engineering (REFSQ), Dagstuhl seminars “Next Generation Domain Specific Conceptual Modeling: Principles and Methods”, and “The Evolution of Conceptual Modeling”. As a teacher, since 1994 he was responsible for 14 university lecture and lab courses in both Ukraine and Austria. His research interests include software, data, and metadata quality, Internet of Things and Industry 4.0, behavior modeling and prediction, methods and tools for information system modeling, analysis, and design, source code analysis and quality, and applying decision theory and optimization techniques to software engineering problems.

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Engineering Serverless Workflow Applications in Federated FaaS

November 29, 2022 | 10:00 am (CET) | Room: S.2.69 | | Alpen-Adria-Universität Klagenfurt

Ass. Prof. Sashko Ristov | Computer Science Department, University of Innsbruck

Abstract: Function-as-a-Service (FaaS) is the latest paradigm of cloud computing in which developers deploy their codes as serverless functions, while the entire underlying platform and infrastructure is completely managed by cloud providers. Each cloud provider offers a huge set of cloud services and many libraries to simplify development and deployment, but only inside their clouds, often in a single cloud region. With such „help“ of cloud providers, users are locked to use resources and services of the selected cloud provider, which are often limited. Moreover, such heterogeneous and distributed environment of multiple cloud regions and providers challenge scientists to engineer cloud applications, often in a form of serverless workflows. In this talk, I will present our design principle „code once, run everywhere, with everything“. In particular, I will present challenges and our approaches and techniques how to program, model, orchestrate, and run distributed serverless workflow applications in federated FaaS.

Bio: Dr. Sashko Ristov is Assistant Professor at the Computer Science Department of University of Innsbruck, Austria. His research interests include engineering distributed applications and systems in federated clouds, in particular automation, simulation, performance modeling, and optimization of serverless workflow applications.

Dr. Ristov has a PhD degree in computer science in 2012 from Ss. Cyril and Methodius University, Skopje, North Macedonia, where he was Assistant professor (2013-2016). He was also Postdoctoral University Assistant at the Distributed and Parallel Systems Group, Computer Science Department, University of Innsbruck, in the period 2016-2022.

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Don’t Treat the Symptom, Find the Cause! Efficient AI Methods for (Interactive) Debugging

October 25, 2022 | 09:00 – 11:00 am (CET) | HS 11 | Patrick Rodler | Alpen-Adria-Universität Klagenfurt

Abstract: In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.

Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.   

In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.

Bio: Patrick Rodler is a postdoctoral researcher at the Department of Artificial Intelligence and Cybersecurity (AICS), University of Klagenfurt. He holds MSc degrees in Technical Mathematics and Computer Science, and received his PhD degree in Computer Science in 2015 from the University of Klagenfurt. As a researcher, he co-authored more than 50 papers, published in prestigious journals such as Web Semantics, Knowledge-Based Systems, Artificial Intelligence, or Information Sciences, and gave 30 talks at renowned venues such as the AAAI Conference on Artificial Intelligence (AAAI), the European Conference on Artificial Intelligence (ECAI), or the Int’l Conference on Knowledge Representation and Reasoning (KR). As a teacher, he was responsible for 26 university courses and lectures, and in 2018 he was awarded a university-wide prize for excellent teaching by the University of Klagenfurt. His research interests include artificial intelligence in general, and model-based diagnosis, intelligent search, heuristic problem solving, as well as knowledge representation and reasoning in particular.

Slides are animated. Please view in Powerpoint presentation mode.

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The Role of Machine Learning in Fluid Network Control and Data Planes

Thursday, October 20, 2022 | 04:00 pm (CET) | Online via Zoom, please register here:

Prof. Dr. Christian Rothenberg | University of Campinas, Brazil

Abstract: As the network softwarization trend started by SDN and NFV keeps evolving, the hardware/software continuum becomes more relevant than ever, offering new offloading/acceleration opportunities at node and network-wide scales. This talk will review evolving transformations behind network softwarization with a special focus on network refactoring and offloading trends leading to “fluid networks planes”, characterized by multiple candidate options for the specific HW/SW embodiment and the location of chained network functions, from the edge to core, from one administrative provider to another, from programmable silicon to portable lightweight virtualized containers. The talk will overview concrete examples from the literature with a special focus on the role of Machine Learning to assist key (automated) decision-making steps.  Lastly, the talk will conclude with a glimpse on ongoing ML work applied to Youtube video QoE prediction in live 5G networks.

Bio: Christian Rothenberg is Associate Professor (tenure-track) and head of the Information & Networking Technologies Research & Innovation Group (INTRIG) at the School of Electrical and Computer Engineering (FEEC) of the University of Campinas (UNICAMP), where he received his Ph.D. in Electrical and Computer Engineering in 2010. From 2010 to 2013, he worked as Senior Research Scientist in the areas of IP systems and networking, leading SDN research at CPQD R&D Center in Telecommunications, Campinas, Brazil. He holds the Telecommunication Engineering degree from the Technical University of Madrid (ETSIT – UPM), Spain, and the M.Sc. (Dipl. Ing.) degree in Electrical Engineering and Information Technology from the Darmstadt University of Technology (TUD), Germany, 2006. Christian has contributed to 07 international patents, co-authored three books, and over 200 scientific publications, including top-tier scientific journals and networking conferences such as SIGCOMM and INFOCOM, altogether featuring 10 000+ citations (h-index: 30+, i10-index: 70+). 

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Edge Intelligence and Protocols for IoT Applications

Friday, July 15, 2022 | 02:00 pm (CET) | Room: S.2.42

Dr. Shajulin Benedict | Indian Institute of Information Technology Kottayam

Abstract: IoT-enabled applications increase tremendously in various sectors, such as transportation, healthcare, education, agriculture, and so forth. These applications sense properties using sensors, perform intelligence, and apply the findings using actuators. Instead of submitting sensor data directly to the cloud, intelligence could be performed with the inclusion of several edge/fog nodes. This improves the privacy and computation time of applications. This talk will provide insights on edge intelligence techniques for such IoT-enabled applications. In addition, a few protocols that are involved in such applications are discussed. 

Bio: Dr. Shajulin Benedict graduated in 2001 from Manonmaniam Sunderanar University, India, with Distinction. In 2004, he received M.E Degree in Digital Communication and Computer Networking from A.K.C.E, Anna University, Chennai. He is the University second rank holder for his masters. He did his Ph.D degree in the area of Grid scheduling under Anna University, Chennai (Supervisor – Dr. V. Vasudevan, Director, Software Technologies Group of TIFAC Core in Network Engineering). After his Ph.D award, he joined a research team in Germany to pursue PostDoctorate under the guidance of Prof. Gerndt. He served as Professor at SXCCE Research Centre of Anna University-Chennai. Later, he visited TUM Germany for teaching Cloud Computing as Guest Professor of TUM-Germany.

Currently, he teaches Internet of Things at the Technical University Munich, Germany; he is affiliated to TUM Germany and to the Indian Institute of Information Technology Kottayam, Kerala, India, an institute of national importance of India. He serves as Director/PI/Representative Officer of AIC-IIITKottayam (Sec.8 Company) for nourishing young entrepreneurs of India. His research interests include IoT Cloud, Performance Analysis of IoT Applications, Cloud Scheduling, Edge Analytics, and so forth.

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Computer Vision techniques for real estate rating

Friday, July 15, 2022 | 10:00 am (CET) | Room: S.2.42

Prof. (FH) PD Dr. habil. Mario Döller | FH-Rector at the University of Applied Science FH Kufstein Tirol

Abstract: Computer vision and AI methods are percolating many branches nowadays. Also in the research field of real estate rating computer vision and AI methods have lead to very interesting innovations. In this research talk, real estate classification by AI-enabled computer vision techniques is discussed.

The talk will give an overview of recent research efforts in the field and focus on latest findings of our research group. This consists of age or heating demand prediction of real estates by photographs as well as the analysis of satellite images for detecting building footprints.

Bio: Prof. (FH) PD Dr. habil. Mario Döller (male) obtained his PhD from the University of Klagenfurt (Austria) in 2004 and his lecturing qualification in computer science from the University of Passau (Germany) in 2012. Currently, Dr. Döller is full professor for multimedia and web based information systems and FH-Rector at the University of Applied Science FH Kufstein Tirol. Dr. Döller is an active member of the MPEG and JPEG consortium (worked as Session Chair on the standardization of the MPEG Query Format).

Besides, he was invited as scientific expert to the Media Annotation Working Group of W3C. Furthermore, he is in the PC of numerous conferences and participated on the organization committee of EuroPar 2002, MUE 2010, SMPT 2010. Dr. Döller is author or co-author of more than 80 scientific publications and has numerous contributions to standardization bodies. Besides, he holds a patent (RDF DB) and awards (e.g. Best Paper Awards). His research in the area of computer vision (e.g., automated real estate rating) has been awarded by MIT Technology Review in the category Most Thought-Provoking Papers 2018. In 2020, his work in automated mobility has been awarded by the 3rd place in the FFG Galileo Masters challenge.

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Towards a Data-driven Identification of Teaching-Patterns

Friday, July 8, 2022 | 02:00 pm (CET) | Room: B01b.0.203, Lakeside Park

Jun. Prof. Dr. Bernhard Standl | Karlsruhe University of Education

Abstract: When it comes to integrating digital technologies into the classroom in higher education, many teachers face similar challenges. Nevertheless, it is difficult for teachers to share experiences because it is usually not possible to transfer successful teaching scenarios directly from one area to another, as subject-specific characteristics make it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns that have been used previously in educational contexts. Patterns can capture proven teaching strategies and describe instructional scenarios in a consistent structure that can be reused. Because priorities for content, methods, and tools are different in each domain, a consensus-tested taxonomy was first developed with the goal of modeling a domain-independent database to collect digital instructional practices. In addition, this presentation will present preliminary insights into a data-driven approach to identifying effective instructional practices from interdisciplinary data as patterns. A web-based application will be developed for this that can both collect teaching/learning scenarios and individually extract scenarios from patterns for a learning platform.

Bio: Bernhard Standl is a tenure-track professor of Informatics Education at the Karlsruhe University of Education. His research focuses on modeling teaching concepts as pedagogical design patterns and on a data-driven identification of effective teaching-learning scenarios and their reuse in practice.

He received his Ph.D. in informatics education from the University of Vienna and where he was also active as a research assistant in educational projects and in a European Union’s funded project and research associate (post-doc) at the Vienna University of Technology. In addition, he worked as an informatics teacher at a high school in Vienna for more than 10 years. He gained international experience as a Fulbright Visiting Scholar at Missouri State University, Springfield, MO, USA.

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Tipping Points and Inference in Complex Systems

Thursday, July 7, 2022 | 02:00 pm (CET) | Room: B4.1.114, Lakeside Park B04b, level 1

Professor Dr. rer. nat. Marc Timme | Strategic Professor & Chair for Network Dynamics TU Dresden, Germany

Abstract: The dynamics of networks enables the function of a variety of systems we rely on every day, from gene regulation and metabolism in the cell to the distribution of electric power and communication of information. Understanding, steering and predicting the function of interacting nonlinear dynamical systems, in particular if they are externally driven out of equilibrium, relies on obtaining and evaluating suitable models, posing at least two major challenges. First, how can we extract key structural system features of networks if only time series data provide information about the dynamics of (some) units?  Second, how can we characterize nonlinear responses of nonlinear multi-dimensional systems externally driven by fluctuations, and consequently, predict tipping points at which normal operational states may be lost? Here we report recent progress on nonlinear response theory extended to predict tipping points and on model-free inference of network structural features from observed dynamics.

This is work with Jose Casadiego, Mor Nitzan, Hauke Haehne, Georg Boerner, Moritz Thuemler and others.

[1] Topical Review: Marc Timme & Jose Casadiego,  J. Phys. A 47:343001 (2014).

[2] Casadiego et al., Nature Comm. 8:2192 (2017).

[3] Nitzan et al., Science Adv.:e1600396 (2017).

[4] Haehne et al., Phys. Rev. Lett. 122:158301 (2019).

[5] Moritz Thuemler et al., submitted (2022).

Bio: Marc Timme studied Physics and Mathematics in Würzburg, Stony Brook (USA) and Göttingen. After work as a postdoctoral research at the Max Planck Institute for Flow Research and as a Research Scholar at Cornell University (USA), he was selected to head a broadly cross-disciplinary Max Planck Research Group on Network Dynamics at the Max Planck Institute for Dynamics and Self-Organization. Marc held  a Visiting Professorship at TU Darmstadt and was visiting faculty at ETH Zurich. He is now Strategic Professor and heads the Chair for Network Dynamics at the Cluster of Excellence Center for Advancing Electronics Dresden (cfaed) and the Institute for Theoretical Physics at TU Dresden. He is also Co-Chair of the Division of Socio-Economic Physics of the German Physical Society (DPG) and since 2018 Honorary Member of Lakeside Labs, Klagenfurt.

With collaborator teams he develops insights about collective nonlinear dynamics of complex systems and their applications in fields of energy and sustainability, mobility, as well as biological and bio-inspired information processing.

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The Computing Continuum: Beyond the Cloud Data Centers

Thursday, June 30th, 2022 | 10:00 (CET) | Room: S.2.69

Dr. Dragi Kimovski, MSc

Abstract: The advent of fog and edge computing has prompted predictions that they will take over the traditional cloud for information processing and knowledge extraction in Internet of Things (IoT) systems. Notwithstanding the fact that fog and edge computing have undoubtedly large potential, these predictions are probably oversimplified and wrongly portray the relations between cloud, fog and edge computing. 

Concretely, 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 IoT applications. These applications have a large spectrum of requirements, burdensome to meet with „distant“ cloud data centers. However, the introduction of the computing continuum raises multiple challenges for 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. These challenges primarily concern the complexity and the large diversity of the devices, managed by different entities (cloud providers, universities, private institutions), which range from single-board computers such as Raspberry Pis to powerful multi-processor servers.

Therefore, in this talk, we will discuss novel algorithms for low latency, scalable, and sustainable computing over heterogeneous resources for information processing and reasoning, thus enabling transparent integration of IoT applications. We will tackle the heterogeneity challenge of dynamically changing topologies of the computing infrastructure and present a novel concept for sustainable processing at scale.

CV: Dragi Kimovski is a  postdoctoral researcher at the Institute of Information Technology (ITEC), University of Klagenfurt, Austria. He earned his doctoral degree in 2013 from the Technical University in Sofia, Bulgaria. He was an assistant professor at  Ohrid University, N. Macedonia, and a senior researcher at the University of Innsbruck, Austria.

He coauthored more than 50 articles in international conferences and journals. His research interests include parallel and distributed computing and multi-objective optimization. He is a work-package leader and scientific coordinator in dozen Horizon 2020 projects (DataCloud, ENTICE, and ASPIDE) and participated in multiple national projects. 

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