Andreas Brüning, Stefan Geisler, Martin Hoefer, Odej Kao
This paper presents a novel concept for resource management in cluster-based image retrieval systems. First, the paper describes image retrieval using static and dynamic feature extraction. The complexity of dynamic feature extraction requires the utilization of powerful parallel architectures and in order to provide the user with reasonable response times. Most existing methods for resource management in parallel image retrieval systems are based on sinlge query execution and do not take quality of service (QoS) aspects into account. This appears not to be practical in large-scale and commercial applications of image databases having a large number of users at any time. In order to allow an efficient utilization of the parallel system and to meet user-defined QoS demands associated with queries, we need to develop a new concept and a novel resource management architecture. Interesting aspects of the model include utility theory, flexible computations, QoS levels, and a hierarchical resource management architecture. Finally, an approach for algorithmic solution is described.
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