Optimization of Computer Networks: Modeling and Algorithms: by Pablo Pav?n Mari?o

By Pablo Pav?n Mari?o

This publication covers the layout and optimization of laptop networks making use of a rigorous optimization method, acceptable to any community technology.  it truly is geared up into elements. partially 1 the reader will how you can version community difficulties showing in machine networks as optimization courses, and use optimization conception to provide insights on them.  4 challenge varieties are addressed systematically – site visitors routing, potential dimensioning, congestion keep an eye on and topology design.

Part 2 pursuits the layout of algorithms that resolve community difficulties just like the ones modeled partially 1.  major methods are addressed – gradient-like algorithms inspiring allotted community protocols that dynamically adapt to the community, or cross-layer schemes that coordinate the cooperation between protocols; and people targeting the layout of heuristic algorithms for long-term static community layout and making plans problems.

Following a hands-on method, the reader can have entry to a wide set of examples in real-life applied sciences like IP, instant and optical networks.  Implementations of types and algorithms might be on hand within the open-source Net2Plan device from which the person could be capable of see how the teachings discovered take genuine shape in algorithms, and reuse or execute them to procure numerical solutions. 

An accompanying hyperlink to the author’s personal Net2plan software program allows readers to provide numerical ideas to a large number of real-life difficulties in desktop networks (www.net2plan.com).  

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Extra info for Optimization of Computer Networks: Modeling and Algorithms: A Hands-On Approach

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Be = EB [ye , ue ] = ∑ue ke y ∕k! 6) In the multiclass context, demands with different sd values can observe different blocking probabilities Be (d). These can be computed for the complete sharing model using the Kaufman–Roberts recursion (proposed independently in [7] and [8]), described in Algorithm 1. Note that, logically, all the demands with the same bandwidth requirements sd perceive the same blocking. 4 plots the blocking probabilities in the single-class traffic case, for a link e with different link capacities (ue ) and load values (????e ).

Also, we denote ???? − (n) as the set of incoming links to node n: ???? − (n) = {e ∈  ∶ b(e) = n}1 . 1 illustrates these concepts. Given a network topology ( , ), a path p in the network is a sequence of links p = (e1 , … , ek ), such that end node of link ei is the initial node of link ei+1 . The initial node of a path p is the initial node of its first link, and we denote it as a( p). The end node of path p, denoted as b( p), is the end node of its last link. For instance, in Fig. 1 p = (e2 , e3 ) is a path with a( p) = n1 and b( p) = n3 .

Note that in single-class networks, we have Bep = Be , ∀p. 8) can be approximated as: ∏ ∑ (1 − Bep ) ≈ Bep Bp = 1 − e∈p e∈p Previous approximation is pessimistic, and its accuracy proportional to the product of blockings (the smaller the blockings the better). Finally, we assume that all the links in a path p sum a quantity equal to hp to the offered traffic. Therefore, in the multiclass case, Algorithm 1 can be used to compute the Bep values, taking as link offered traffic the sum of the traffics in the traversing paths.

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