Read e-book online Analytical and Stochastic Modeling Techniques and PDF

By Sofiane Ouazine, Karim Abbas (auth.), Alexander Dudin, Koen De Turck (eds.)

ISBN-10: 3642394078

ISBN-13: 9783642394072

ISBN-10: 3642394086

ISBN-13: 9783642394089

This publication constitutes the refereed court cases of the 20 th foreign convention on Analytical and Stochastic Modelling and purposes, ASMTA 2013, held in Ghent, Belgium, in July 2013. The 32 papers provided have been conscientiously reviewed and chosen from various submissions. the point of interest of the papers is at the following software issues: complicated structures; desktop and knowledge platforms; communique platforms and networks; instant and cellular structures and networks; peer-to-peer software and companies; embedded platforms and sensor networks; workload modelling and characterization; highway site visitors and transportation; social networks; measurements and hybrid options; modeling of virtualization; energy-aware optimization; stochastic modeling for platforms biology; biologically encouraged community design.

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Read Online or Download Analytical and Stochastic Modeling Techniques and Applications: 20th International Conference, ASMTA 2013, Ghent, Belgium, July 8-10, 2013. Proceedings PDF

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Extra info for Analytical and Stochastic Modeling Techniques and Applications: 20th International Conference, ASMTA 2013, Ghent, Belgium, July 8-10, 2013. Proceedings

Example text

The curves corresponding to the true relative error and the relative error estimation with the use of (14) are given in Figure 6 depending on the size of the quantum. As one can see from the example the proposed bound is quite tight and can be used for preliminary estimation of negative effect connected with possible inaccuracy in system parameters definition. Also it allows choosing an appropriate quantum size depending on preassigned level of relative error. 1 10 Real error Upper bound 0 Relative error 10 −1 10 −2 10 −3 10 −4 10 10^−1 10^−2 10^−3 10^−4 Quant size Fig.

In Section 5 we show examples for which we considered only the topology to guide the choice of the DAG and others for which more factors were taken into account. , for every node v ∈ V summing P {∧i∈D(v) (Qi = xi )} for every possible values of xi , i ∈ D(v), gives one. , P {∧j∈D(v) (Qj = yj )}. By considering the derivative dP {Qi = xi }/dt which can be computed based on both u and v by summation of their associated ODEs given in (5), it is easy to show that the different ways of calculating P {Qi = xi } lead to the same result.

6, sim. 4, sim. 5, sim. 6, sim. 5 2 0 0 0 50 100 150 200 250 0 50 100 150 200 250 Fig. 5. Multi-path: expectations (left) and variances (right) of the stations 4, 5 and 6 vs. 4, sim. 5, sim. 6, sim. 4, sim. 5, sim. 6, sim. 30 8 25 6 20 15 4 10 2 5 0 0 0 50 100 150 200 250 0 50 100 150 200 250 Fig. 6. Multi-path: expectations (left) and variances (right) of the stations 4, 5, and 6 vs. 6 initial state 1, qpf initial state 2, qpf initial state 1, sim. initial state 2, sim. 4 initial state 1, qpf initial state 2, qpf initial state 1, sim.

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Analytical and Stochastic Modeling Techniques and Applications: 20th International Conference, ASMTA 2013, Ghent, Belgium, July 8-10, 2013. Proceedings by Sofiane Ouazine, Karim Abbas (auth.), Alexander Dudin, Koen De Turck (eds.)


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