Communication Networks: A Concise Introduction (Synthesis by Jean Walrand, Shyam Parekh

By Jean Walrand, Shyam Parekh

This e-book effects from a long time of training an higher department direction on verbal exchange networks within the EECS division at collage of California, Berkeley. it's encouraged by means of the perceived want for an simply obtainable textbook that places emphasis at the center recommendations in the back of present and subsequent new release networks. After an summary of ways present day web works and a dialogue of the most ideas in the back of its structure, we speak about the main principles at the back of Ethernet, WiFi networks, routing, internetworking and TCP. To make the booklet as self contained as attainable, short discussions of chance and Markov chain thoughts are integrated within the appendices. this is often via a quick dialogue of mathematical versions that offer perception into the operations of community protocols. subsequent, the most rules in the back of the hot iteration of instant networks in keeping with WiMAX and LTE, and the proposal of QoS are provided. A concise dialogue of the actual layer applied sciences underlying a variety of networks can also be integrated. eventually, a sampling of themes is gifted that could have major impact at the destiny evolution of networks together with overlay networks like content material supply and peer-to-peer networks, sensor networks, disbursed algorithms, Byzantine contract and resource compression. desk of Contents: the net / ideas / Ethernet / WiFi / Routing / Internetworking / delivery / versions / WiMAX & LTE / QOS / actual Layer / extra themes

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The first one has N1 equally likely outcomes and the second has N2 equally likely outcomes. The event A is that the first experiment has an outcome that is in a set of M1 of the N1 outcomes. The event B is that the second experiment has an outcome in a set of M2 of the N2 outcomes. Assume also that the two experiments are performed ‘independently’ so that the N1 × N2 pairs of outcomes of the two experiments are all equally likely. Then we find that the event ‘A and B’ corresponds to M1 × M2 possible outcomes out of N1 × N2 , so that P (A and B) = M1 M2 M1 × M 2 = × = P (A)P (B).

But we know that this fraction is p. Hence, p = 1/N , or N = 1/p. ” Similarly, if the probability of winning the California lottery is one in one million per trial, then one has to play one million times, on average, before the first win. Playing once a week, one can expect to wait about nineteen thousand years. 7 HUB ETHERNET As we explained in the section on Hub Ethernet, the stations wait a random multiple of T = 2P ROP until they attempt to transmit. In a simplified model, one may consider that the probability that one station transmits in a given time slot with duration 2P ROP is approximately 1/e.

We describe some methods that the Internet uses for scalability. 1 LOCATION-BASED ADDRESSING We explained in the previous chapter that the IP addresses are based on the location of the devices, in a scheme similar to the telephone numbers. One first benefit of this approach is that it reduces the size of the routing tables, as we already discussed. 3 shows M = N 2 devices that are arranged in N groups with N devices each. Each group is attached to one router. 3: A simple illustration of location-based addressing.

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