Design and Performance of a Media Gateway Trader

Rafaelsen, Hans Ole; Eliassen, Frank

doi:10.1007/3-540-36124-3_46

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…A common way to model multimedia applications is to represent media transport and processing by means of flow graphs, where the nodes represent media processing modules and the edges represent media flows. Such an approach has been taken in the Java media framework (JMF) [Sun Microsystems 1999], the Infopipe project [Black et al 2002], the MediaMesh architecture [Amini et al 2000], media gateway systems [Ooi and van Renesse 2001;Rafaelsen and Eliassen 2002], and the Parallel software-only video effects processing system (PSVP) [Mayer-Patel and Rowe 1998;1999a;1999b]. The graph building processes in both JMF and MediaMesh do not take into account more than one computer.…”

Section: Media Streaming and Processingmentioning

confidence: 99%

Real-time video content analysis

Eide

Granmo

Eliassen

et al. 2006

ACM Trans. Multimedia Comput. Commun. Appl.

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Real-Time content-based access to live video data requires content analysis applications that are able to process video streams in real-time and with an acceptable error rate. Statements such as this express quality of service (QoS) requirements. In general, control of the QoS provided can be achieved by sacrificing application quality in one QoS dimension for better quality in another, or by controlling the allocation of processing resources to the application. However, controlling QoS in video content analysis is particularly difficult, not only because main QoS dimensions like accuracy are nonadditive, but also because both the communication-and the processing-resource requirements are challenging.This article presents techniques for QoS-aware composition of applications for real-time video content analysis, based on dynamic Bayesian networks. The aim of QoS-aware composition is to determine application deployment configurations which satisfy a given set of QoS requirements. Our approach consists of: (1) an algorithm for QoS-aware selection of configurations of feature extractor and classification algorithms which balances requirements for timeliness and accuracy against available processing resources, (2) a distributed content-based publish/subscribe system which provides application scalability at multiple logical levels of distribution, and (3) scalable solutions for video streaming, filtering/transformation, feature extraction, and classification.We evaluate our approach based on experiments with an implementation of a real-time motion vector based object-tracking application. The evaluation shows that the application largely behaves as expected when resource availability and selections of configurations of feature extractor and classification algorithms vary. The evaluation also shows that increasing QoS requirements can be met by allocating additional CPUs for parallel processing, with only minor overhead.

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