Abstract
More and more distributed multimedia applications are becoming an integral part of our computing and communication environment. To achieve this goal, the multimedia applications must be delivered with high Quality of Service (QoS). This is a challenge as the distributed multimedia applications run on top of general purpose operating systems and networks that have been developed for best-effort data processing and transmission. Hence, they suffer high level of perturbations in resource allocation when handling continuous media. To solve this challenge, many approaches for QoS adaptation to the distributed multimedia system context have been proposed.
Any QoS adaptation approach needs to address the following problems: (1) QoS specification to capture users’ QoS requirements and preferences; (2) QoS mapping to translate representations of QoS at different layers of a distributed multimedia system; and (3) QoS control to provide real-time traffic control and shaping of flows, based on available network bandwidth.
To address the first problem, QoS adaptation approaches must often deal with varying user’s point of view of quality, taking in account the vagueness and subjectivity inherent to the user’s evaluation of quality. The usual solutions for the second problem use measurement-based techniques, obtained from samples of applications or simulations of traffics patterns, which is a time-consuming process. Finally, to solve the third problem, QoS adaptation approaches must deal with the considerable uncertainty related to congestion determination, as the network load oscillates very suddenly and drastically.
In this context, soft computing techniques, such as fuzzy control, artificial neural networks and neuro-fuzzy control, seem a promising approach to be used for QoS adaptation, since these techniques deal with vagueness and uncertainty.
In this chapter, we discuss how soft computing techniques can be used for QoS adaptation in distributed multimedia systems, specially in assisting QoS control of multimedia end-to-end delivery activities through Fuzzy Logic.
This work was funded by the FAPERGS funding agency — Brazil.
This work has been partially supported by a grant from CNPq — Brazil.
This work was funded by the DARPA funding agency, under contract number F30602–97–2-0121, and by the NSF funding agency, under contract number CCR-9988199 grant, both USA grants.
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Koliver, C., Farines, JM., Nahrstedt, K. (2004). QoS Adaptation Based on Fuzzy Theory. In: Soft Computing in Communications. Studies in Fuzziness and Soft Computing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45090-0_12
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DOI: https://doi.org/10.1007/978-3-540-45090-0_12
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