Bio-Inspired Models of Network, Information, and Computing by Gianni A. Di Caro, Guy Theraulaz (eds.)

By Gianni A. Di Caro, Guy Theraulaz (eds.)

This booklet constitutes the completely refereed post-conference complaints of the seventh foreign convention on Bio-Inspired types of community, details and Computing platforms (Bionetics 2012), held in Lugano, Switzerland, in December 2012. The 23 revised complete papers provided have been rigorously reviewed and chosen from forty submissions. They disguise themes equivalent to networking, robotics and neural networks, molecular scale and bioinformatics, optimization and bio-inspired modeling in numerous fields.

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Further we use fEx (x|θEx ) = λEx e− λEx x x ≥ 0 0 x<0 (3) for the exponential distribution density with the rate parameter λEx > 0 and fδ (x) = δ(x) (4) The Reactive ASR-FA 37 for the formal description of the single point distribution. We sometimes group the parameters of each distribution, denoting then αΓ = (λΓ , ΓΓ ) and αEx = (λEx ). For a single hop path, the heuristics for our model follows an approximation of a M/D/1 queue (Poisson arrival process, constant service time, 1 queue) [6]. We use the delta distribution to fit the delay of packets that did not wait in any queue, the gamma distribution approximates the M/D/1 delay for small and medium delays [18], and an exponential distribution approximates the tail of the delay distribution of a M/D/1 queue.

As shown in Fig. 3, there is a difference in slope between the analytical result and the simulation result. 1 −∈w∀a is larger in the simulation than in the analysis. Given the variance σa2 , the load on a network in the case of the analysis, which can be derived from Eq. (7), is smaller than that of the simulation, which can be derived as (a+9)/15 considering that the amount of background traffic is 9 Mbps and the capacity of the bottleneck link is 15 Mbps. In general, when the sending rate increases, the end-to-end delay becomes larger in a congested network than in an unloaded network.

Some mechanisms such as a playout buffer can be incorporated with our proposal. From a view point of the attractor perturbation principle, the behavior of other incorporated mechanism is another origin of fluctuation of a network system. As such, it only changes the variance of end-to-end delay observed by our rate control mechanism. Then, we can expect that our proposal can work well without any tuning, modification, or extension. Acknowledgement. This research was supported in part by Grand-in-Aid for Scientific Research (B) 22300023 of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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