Abstract
With the increasing availability of streaming applications from mobile devices to dedicated sensors, understanding how such streaming content can be processed within some time threshold remains an important requirement. We investigate how a computational infrastructure responds to such streaming content based on the revenue per stream – taking account of the price paid to process each stream, the penalty per stream if the pre-agreed throughput rate is not met, and the cost of resource provisioning within the infrastructure. We use a token-bucket based rate adaptation strategy to limit the data injection rate of each data stream, along with the use of a shared token-bucket to enable better allocation of computational resource to each stream. We demonstrate how the shared token-bucket based approach can enhance the performance of a particular class of applications, whilst still maintaining a minimal quality of service for all streams entering the system.
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Bañares, J.Á., Rana, O.F., Tolosana-Calasanz, R., Pham, C. (2013). Revenue Creation for Rate Adaptive Stream Management in Multi-tenancy Environments. In: Altmann, J., Vanmechelen, K., Rana, O.F. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2013. Lecture Notes in Computer Science, vol 8193. Springer, Cham. https://doi.org/10.1007/978-3-319-02414-1_9
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DOI: https://doi.org/10.1007/978-3-319-02414-1_9
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