Prof. Dr.-Ing. Franz Hauck

Franz Hauck
Prof. Dr.-Ing. Franz Hauck
Stellv. Institutsdirektor
Institut für Verteilte Systeme
Institut für Verteilte Systeme
Universität Ulm
Albert-Einstein-Allee 11
89081 Ulm
Raum: O27 348
Telefon: +4973150-24143


jeden Donnerstag um 15:00-16:00 Uhr in meinem Büro O27-348


Prof. Dr.-Ing. Franz J. Hauck

Prof. Hauck studierte Informatik an der Universität Erlangen-Nürnberg. Nach zwei Jahren in der Industrie promovierte und habilitierte er sich an der Universität Erlangen-Nürnberg unterbrochen von einem einjährigen Auslandsaufenthalt an der Vrije Universiteit Amsterdam. Seit 2002 lehrt und forscht er an der Universität Ulm als Professor für Verteilte Systeme am gleichnamigen Institut.

Seine Forschungsinteressen sind Middleware-Systeme für spezielle Aufgaben. Der Fokus liegt auf fehlertoleranten Serversystemen, vor allem auf State-Machine Replication (SMR).

In der Lehre vertritt Prof. Hauck die Themengebiete Betriebssysteme und Verteilte Systeme. Seine aktuellen Veranstaltungen finden Sie auf der Lehreseite des Instituts. Alle seine Lehrveranstaltungen finden sich auf einer Detailseite.

Prof. Hauck ist Mitglied der ACM, der Gesellschaft für Informatik, GI und deren Fachgruppen Betriebssysteme , KuVS und FERS sowie von EuroSys.

Er ist außerdem gewähltes Mitglied des Fakultätsrats seiner Fakultät, bestelltes Mitglied der Informatik für die Gemeinsame Kommission Lehramt und damit gleichzeitig Mitglied in der Studienkommission, dem Prüfungsausschuss und dem Zulassungsausschuss für das Lehramt. Im Prüfungsausschuss Lehramt ist er gewählter Vorsitzender.

Seine letzten Publikationen:

Mehdi, M., Hauck, F.J., Pryss, R. and Schlee, W. 2024. Mobile health solutions for Tinnitus. Textbook on Tinnitus (Mar. 2024), 723–738.
Modern mobile devices are mainstream and ubiquitous devices. The widespread adoption of mobile devices has resulted in surge of mobile applications (apps) hosted on marketplaces (app stores) of several mobile platforms. Besides other benefits, these apps are also applied in healthcare-related and medical use, for instance, in case of tinnitus, where tinnitus disorder is associated with the perception of ringing sound without external sound source. In particular, for tinnitus, these apps allow provision of tinnitus-related relief, self-help, and general management. The collective aim of this chapter is to foster and report on Mobile Health (mHealth) solutions, in particular mobile apps within the tinnitus context. First, this chapter provides an up-to-date overview of existing mHealth apps available for major mobile platforms. Second, this chapter provides deep insights into quality and effectiveness of said mobile apps for tinnitus treatment and management. Finally, this chapter provides discussions in relation to the tinnitus-related mHealth apps.
Hauck, F.J. and Heß, A. 2024. Linearizability and state-machine replication. Workshop on Resilient Oper. - Byz. Fault Tol. and State-Machine Repl. – ROBUST (Mar. 2024).
Heß, A. and Hauck, F.J. 2024. A framework for consensus-agnostic state-machine replication based on threshold signatures. Workshop on Resilient Oper. - Byz. Fault Tol. and State-Machine Repl. – ROBUST (Mar. 2024).
Köstler, J., Reiser, H.P., Hauck, F.J. and Habiger, G. 2023. Fluidity: location-awareness in replicated state machines. 38th ACM/SIGAPP Symp. on Appl. Comp. – SAC (Mar. 2023).
In planetary-scale replication systems, the overall response delay is greatly influenced by the geographical distances between client and server nodes. Current systems define the replica locations statically during startup time. However, the selected locations might be suboptimal for the clients, and the client request origin distribution may change over time, so a different replica placement may provide lower overall request latencies. In this work, we propose a locationaware replicated state machine that is able to adapt the geographic location of its replicas dynamically during runtime to locations geographically closer to client request origins. Our prototype is able to observe emerging optimization potentials and to reduce the overall request latency for the majority of clients by adapting its replica locations to the time-dependent optimum placement during real-world use case evaluations, whereby the absolute performance gain is dependent on the respective usage scenario.
Heß, A. and Hauck, F.J. 2023. Towards a Cloud Service for State-Machine Replication. Tagungsband des FG-BS Frühjahrstreffens 2023 (Bonn - Germany, 2023).
State-machine replication (SMR) is a well-known technique to achieve fault tolerance for services that require high availability and fast recovery times. While the concept of SMR has been extensively investigated, there are still missing building blocks to provide a generic offer, which automatically serves applications with SMR technology in the cloud. In this work, we introduce a cloud service architecture that enables automatic deployment of service applications based on customer-friendly service parameters, which are mapped onto an internal configuration that comprises the number of replicas, tolerable failures, and the consensus algorithm, amongst other aspects. The deployed service configuration is masked to large extent with the use of threshold signatures. As a consequence, a reconfiguration in the cloud deployment does not affect the client-side code. We conclude the paper by discussing open engineering questions that need to be addressed in order to provide a productive cloud offer.

Weitere Informationen finden sich auf anderen Seiten: vollständige Publikationsliste, Doktoranden. Weitere Details zu Projekten und gehaltenen Lehrveranstaltungen finden sich auf einer Detailseite.