Fernuniv., Diss.--Hagen, 2006.

Universiẗat, Diss.--Erlangen-Nürnberg, 2005.

Zugl.: Leipzig, Universiẗat Leipzig, Diss., 2008.

Univ., Diss.--Mannheim, 2002.

Terminplaner, Adreßbuch-Anwendungen usw. werden derzeit auf einer Vielzahl unterschiedlicher Plattformen genutzt. Daher sind Abgleichverfahren sowohl für mehrere Rechner zu einer Person (Desktop...PDA) als auch für Team-Koordinierungen nötig. Im Rahmen dieser Arbeit sollen die existierenden Standards und Lösungen analysiert werden. Aufbauend auf die gefundenen Defizite und Richtungen der Standardisierung (SyncML...) ist ein eigener Prototyp zu realisieren (Plattformen, Werkzeuge und Implementierungssprachen sind mit dem Betreuer abzustimmen).

The resource demand on embedded devices is constantly growing. This is caused by the sheer explosion of software based functions in embedded systems, that are growing far faster than the resources of the single-core and multi-core embedded processors. As one of the limitation is the computing power of the processors we need to explore ways to use this resource more efficiently. We identified that during the run-time of the embedded devices the resource demand of the software functions is permanently changing dependent on the device situation. To enable an embedded device to take advantage of this dynamic resource demand, the allocation of the software functions to the processor must be handled by a scheduler that is able to evaluate the resource demand of the software functions in relation to the device situation. This marks a change in embedded devices from static defined software systems to dynamic software systems. Above that we can increase the efficiency even further by extending the approach from a single device to a distributed or networked system (many-core system). However, existing approaches to deal with dynamic resource allocation are focused on individual devices and leave the optimization potential of manycore systems untouched. Our concept will extend the existing Hierarchical Asynchronous Multi-Core Scheduler (HAMS) concept for individual devices to many-core systems. This extension introduces a dynamic situation aware scheduler for many-core systems which take the current workload of all devices and the system-situation into account. With our approach, the resource efficiency of an embedded many-core system can be increased. The following paper will explain the architecture and the expected results of our concept.

The demand for so-called living or real-time data warehouses is increasing in many application areas such as manufacturing, event monitoring and telecommunications. In these fields, users normally expect short response times for their queries and high freshness for the requested data. However, meeting these fundamental requirements is challenging due to the high loads and the continuous flow of write-only updates and read-only queries that might be in conflict with each other. Therefore, we present the concept of workload balancing by election (WINE), which allows users to express their individual demands on the quality of service and the quality of data, respectively. WINE exploits these information to balance and prioritize both types of transactions—queries and updates—according to the varying user needs. A simulation study shows that our proposed algorithm outperforms competing baseline algorithms over the entire spectrum of workloads and user requirements.

The demand for so-called living or real-time data warehouses is increasing in many application areas, including manufacturing, event monitoring and telecommunications. In fields like these, users normally expect short response times for their queries and high freshness for the requested data. However, it is truly challenging to meet both requirements at the same time because of the continuous flow of write-only updates and read-only queries as well as the latency caused by arbitrarily complex ETL processes. To optimize the update flow in terms of data freshness maximization and load minimization, we propose two algorithms - local and global scheduling - that operate on the basis of different system information. We want to discuss the benefits and drawbacks of both approaches in detail and derive recommendations regarding the optimal scheduling strategy for any given system setup and workload.

The resource demand on embedded devices is constantly growing. This is caused by the sheer explosion of software based functions in embedded systems, that are growing far faster than the resources of the single-core and multi-core embedded processors. As one of the limitation is the computing power of the processors we need to explore ways to use this resource more efficiently. We identified that during the run-time of the embedded devices the resource demand of the software functions is permanently changing dependent on the device situation. To enable an embedded device to take advantage of this dynamic resource demand, the allocation of the software functions to the processor must be handled by a scheduler that is able to evaluate the resource demand of the software functions in relation to the device situation. This marks a change in embedded devices from static defined software systems to dynamic software systems. Above that we can increase the efficiency even further by extending the approach from a single device to a distributed or networked system (many-core system). However, existing approaches to deal with dynamic resource allocation are focused on individual devices and leave the optimization potential of manycore systems untouched. Our concept will extend the existing Hierarchical Asynchronous Multi-Core Scheduler (HAMS) concept for individual devices to many-core systems. This extension introduces a dynamic situation aware scheduler for many-core systems which take the current workload of all devices and the system-situation into account. With our approach, the resource efficiency of an embedded many-core system can be increased. The following paper will explain the architecture and the expected results of our concept.

Floor-based Automated Guided Vehicles (AGV) are important for automating transportation tasks in semiconductor manufacturing facilities. They are either used as homogenous systems in low throughput areas (see Ndiaye et al., 2016) or complementarily with ceiling-mounted Overhead Hoist Transport (OHT) systems (see Keil et al., 2018). AGV systems provide the option to exchange transport carriers between its vehicles during transport execution. This increases the flexibility to allocate tasks to the vehicles and could lead to better system performance.