Institut für Verteilte Systeme

Unser Institut beschäftigt sich mit Themen wie Skalierbarkeit, Zuverlässigkeit, Sicherheit und Datenschutz, Selbstorganisation und Beherrschbarkeit von Komplexität in Verteilten Systemen in einer Vielzahl von Einsatzszenarien wie Cloud-Computing oder Fahrzeug-Fahrzeug-Kommunikation.

In der Lehre decken wir das gesamte Spektrum von Rechnernetzen, über verteilte Systeme bis hin zu Sicherheit und Privacy-Schutz ab.

Unsere letzten Publikationen

Lukaseder, Thomas; Fiedler, Jessika; Kargl, Frank
Performance Evaluation in High-Speed Networks by the Example of Intrusion Detection Systems
11. DFN-Forum Kommunikationstechnologien,
Juni 2018
Matousek, Matthias; Yassin, Mahmoud; Al-Momani, Ala'a; van der Heijden, Rens W.; Kargl, Frank
Robust Detection of Anomalous Driving Behavior
IEEE 87th Vehicular Technology Conference (VTC)
Juni 2018
Erb, Benjamin; Meißner, Dominik; Steer, Benjamin A.; Margan, Domagoj; Kargl, Frank; Cuadrado, Felix; Pietzuch, Peter
GraphTides: A Framework for Evaluating Stream-based Graph Processing Platforms
Proceedings of the 1st Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)
Juni 2018

Zusammenfassung: Stream-based graph systems continuously ingest graph-changing events via an established input stream, performing the required computation on the corresponding graph. While there are various benchmarking and evaluation approaches for traditional, batch-oriented graph processing systems, there are no common procedures for evaluating stream-based graph systems. We, therefore, present GraphTides, a generic framework which includes the definition of an appropriate system model, an exploration of the parameter space, suitable workloads, and computations required for evaluating such systems. Furthermore, we propose a methodology and provide an architecture for running experimental evaluations. With our framework, we hope to systematically support system development, performance measurements, engineering, and comparisons of stream-based graph systems.

Meißner, Dominik; Erb, Benjamin; Kargl, Frank; Tichy, Matthias
retro-λ: An Event-sourced Platform for Serverless Applications with Retroactive Computing Support
Proceedings of the 12th ACM International Conference on Distributed Event-Based Systems
Juni 2018

Zusammenfassung: State changes over time are inherent characteristics of stateful applications. So far, there are almost no attempts to make the past application history programmatically accessible or even modifiable. This is primarily due to the complexity of temporal changes and a difficult alignment with prevalent programming primitives and persistence strategies. Retroactive computing enables powerful capabilities though, including computations and predictions of alternate application timelines, post-hoc bug fixes, or retroactive state explorations. We propose an event-driven programming model that is oriented towards serverless computing and applies retroaction to the event sourcing paradigm. Our model is deliberately restrictive, but therefore keeps the complexity of retroactive operations in check. We then introduce retro-λ, a runtime platform that implements the model and provides retroactive capabilites to its applications. While retro-λ only shows negligible performance overheads compared to similar solutions for running regular applications, it enables its users to execute retroactive computations on the application histories as part of its programming model.

Mödinger, David; Kopp, Henning; Kargl, Frank; Hauck, Franz J.
Towards Enhanced Network Privacy for Blockchains
Short research statement for the DSN Workshop on Byzantine Consensus and Resilient Blockchains 2018
Juni 2018

Zusammenfassung: Privacy aspects of blockchains have gained attention as the log of transactions can be view by any interested party. Privacy mechanisms applied to the ledger can be undermined by attackers on the network level, resulting in deanonymization of the transaction senders. We discuss current approaches to this problem, e.g. Dandelion, sketch our own approach to provide even stronger privacy mechanisms and discuss the challenges and open questions for further research in this area.

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