Institut für Organisation und Management von Informationssystemen

Unser Institut beschäftigt sich mit Fragestellungen, wie autonomem Management von Informationsstrukturen, effizienter Realisierung von Cloud- oder Clustersystemen und wie dabei eine Kooperation von Anwendung, Middleware und Hardwaremanagement bei der Bereitstellung der Dienste erreicht werden kann.

Für weitere Informationen besuchen Sie unsere Seite zu den Forschungsschwerpunkten und den laufenden Projekten.

 

Unsere letzten Publikationen


Seybold, Daniel; Wagner, Nicolas; Erb, Benjamin; Domascka, Jörg
Is Elasticity of Scalable Databases a Myth?
4th Workshop on Scalable Cloud Data Management
November 2016
akzeptiert
Seybold, Daniel
Towards Cloud-centric Distributed Database Evaluation
In 2nd Cloud Forward, Editor,
Oktober 2016

Zusammenfassung: The evolvement of cloud computing pushed the rethinking of traditional web service architecture from monolithic structures to distributed systems, which will benefit from the cloud offers such as resource pooling or rapid elasticity. Whereas the distribution of the mostly stateless business logic services fits well for distribution in the cloud, the distribution of stateful database services is more challenging. Hence in parallel to the evolvement of cloud computing, distributed databases moved in the focus of academia and industry with the result of variety of distributed database systems, which can be classified in relational databases, NoSQL and NewSQL database systems. Theoretically the current representatives of these three database system classes claim to provide elasticity and “unlimited” horizontal scalability. As the characteristics elasticity and scalability match the cloud offerings, distributed databases seem to be a perfect match for implementing Database-as-a-Service systems (DBaaS). However, academia and industry have already proven significant differences in the elasticity and scalability of distributed databases in specific scenarios. As the cloud stack adds adds another level of complexity to the evaluation of distributed databases, an advanced evaluation framework is required to enable comparable and reproducible evaluations of distributed databases in the cloud. In this context a new approach towards a cloudcentric evaluation framework for distributed databases is proposed, encompassing a model-driven evaluation methodology, an evaluation execution framework and a cloud-centric knowledge base for distributed database scalability and elasticity.

Hauser, Christopher B.
Self-organized and Flexible Distributed Resource Cluster: ‘DisResc’
In 2nd Cloud Forward, Editor,
Oktober 2016

Zusammenfassung: The idea of DisResc is to place workloads with different requirements on heterogeneous resources, while both the requirements and the resources are considered. The overall goals are to better utilise (heterogeneous) data centres, provide better user experiences by selecting the best matching hardware for a workload, and to allow different workload types like virtual machines, containers or HPC jobs side by side in one data centre. DisResc is the vision to build a cluster management software for cloud and hpc workloads, running in parallel on a heterogeneous physical data centre. DisResc enabled compute nodes should i) run virtual machines, containers or HPC jobs, ii) compile time-based behaviour profiles of their workloads, in order to iii) detect suboptimal situations like over/under utilization. The resource utilisation should consider multiple metrics like processor, memory, disk, and network utilisation. The cluster should communicate in a peer-based manner to agree on the best fitting placement of new workloads, and workloads of nodes in a suboptimal state. This non-centralised approach can remove single points for management and monitoring, which are usually found in Cloud and HPC clusters. Beside the distributed and stateful compute nodes, stateless gateway nodes allow user interactions with the cluster. The two main challenges and open questions of DisResc are first the compilation of a time-based behaviour profile out of monitoring data, and second to define a distributed consensus algorithm for determining a best fitting node for a behaviour profile.

Seybold, Daniel; Woitsch, Robert; Domaschka, Jörg; Wesner, Stefan
BPaaS Execution in CloudSocket
Springer CCIS,
September 2016
akzeptiert
Hauser, Christopher B.; Tsitsipas, Athanasios; Domaschka, Jörg
Context-Aware Cloud Topology Optimization for OpenStack
Springer CCIS,
September 2016
akzeptiert
Griesinger, Frank; Seybold, Daniel; Domaschka, Jörg; Kritikos, Kyriakos; Woitsch, Robert
A DMN-based Approach for Dynamic Deployment Modelling of Cloud Applications
Service Oriented and Cloud Computing
September 2016
akzeptiert
Seybold, Daniel; Domaschka, Jörg; Rossini, Alessandro; Hauser, Christopher B.; Griesinger, Frank; Tsitsipas, Athanasios
Experiences of models@run-time with EMF and CDO
ACM Digital Library,
August 2016
akzeptiert
Tsitsipas, Athanasios; Hauser, Christopher B.; Domaschka, Jörg; Wesner, Stefan
Towards Usage-based Dynamic Overbooking in IaaS Clouds
Proceedings of the 13th International Conference on Economics of Grids, Cloud, Systems and Services (GECON 2016)
Juli 2016
akzeptiert
Hauck, Franz J.; Domaschka, Jörg; Habiger, Gerhard
UDS: A novel and flexible scheduling algorithm for deterministic multithreading
Proceedings of the 35th Symposium on Reliable Distributed Systems (SRDS)
Juni 2016
akzeptiert
Sarker, Mitalee; Siersch, Jan; Wesner, Stefan; Khan, Arslan
Towards a method integrating Virtual Switch Performance into Data Centre Design
The Fifteenth International Conference on Networks, ICN 2016
Lisbon, Portugal
Februar 2016
akzeptiert

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