By Masakiyo Miyazawa (auth.), Wuyi Yue, Yataka Takahashi, Hideaki Takagi (eds.)

ISBN-10: 0387097023

ISBN-13: 9780387097022

ISBN-10: 0387097031

ISBN-13: 9780387097039

Advances in Queueing conception and community Applications provides numerous valuable mathematical analyses in queueing idea and mathematical types of key applied sciences in stressed out and instant communique networks equivalent to channel entry controls, net purposes, topology development, power saving schemes, and transmission scheduling. In 16 top of the range chapters, this paintings presents novel rules, new analytical versions, and simulation and experimental effects through specialists within the box of queueing idea and community applications.

The textual content serves as a cutting-edge reference for quite a lot of researchers and engineers engaged within the fields of queueing idea and community purposes, and will additionally function supplemental fabric for complex classes in operations examine, queueing idea, functionality research, site visitors thought, in addition to theoretical layout and administration of verbal exchange networks.

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W. Byers, M. Luby, M. Mitzenmacher, and A. Rege, A digital fountain approach to reliable distribution of bulk data, in Proc. SIGCOMM, pp. 56–67, 1998. [Online]. html. 3. K. Thirumalai, J. F. Paris, and D. D. E. Long, Tabbycat: an inexpensive scalable server for video-on-demand, in Proc. IEEE International Conference on Communications, ICC’03, vol. 2, pp. 896–900, 2003. 4. M. Tran and W. Tavanapong, Peers-assisted dynamic content distribution networks, in Proc. The IEEE Conference on Local Computer Networks, pp.

Thus, we can see a fine balance here. 04988, well below the content size s = 100. Let us study in some detail the optimal merging limit. 8). Letting λ → 0, we have x0 (λ ) → s, which means for a smaller request rate we cannot count on the following requests, so “be a child whenever you can” is the best strategy. On the contrary, for large λ , we have x0 (λ ) → 0, as λ → ∞. For a larger request rate, you can always expect the following requests. ” If we use unicast only, instead of the combination of unicast and multicast, the average streaming rate is ρ .

Assume the requests to the content of size s is a Poisson process with the rate λ . Given the merging limit time x, the average download rate is obtained by b(x) = 2λ s + λ 2 x2 . 7) The function b(x) is indeed a convex function (see Fig. 4), so we have x0 which minimizes b(x) as x0 = (1 + 2λ s)1/2 − 1 . 9) where ρ = λ s corresponds to the scale of this streaming service. Proof. We know that S(t) is a renewal reward process from Sect. 2. 1] is an extension of the strong law of large numbers to the renewal process.

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Advances in Queueing Theory and Network Applications by Masakiyo Miyazawa (auth.), Wuyi Yue, Yataka Takahashi, Hideaki Takagi (eds.)


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