Chapter 7: End-to-End Data

It is a capital mistake to theorize before one has data. —Sir Arthur Conan Doyle

Problem: What Do We Do with the Data?

From the network's perspective, application programs send messages to each other. Each of these messages is just an uninterpreted string of bytes. From the application's perspective, however, these messages contain various kinds of data—arrays of integers, video frames, lines of text, digital images, and so on. In other words, these bytes have meaning. We now consider the problem of how best to encode the different kinds of data that application programs want to exchange into byte strings. In many respects, this is similar to the problem of encoding byte strings into electromagnetic signals that we saw in an earlier chapter.

Thinking back to our discussion of encoding, there are essentially two concerns. The first is that the receiver be able to extract the same message from the signal as the transmitter sent; this is the framing problem. The second is making the encoding as efficient as possible. Both of these concerns are also present when encoding application data into network messages.

In order for the receiver to extract the message sent by the transmitter, the two sides need to agree to a message format, often called the presentation format. If the sender wants to send the receiver an array of integers, for example, then the two sides have to agree what each integer looks like (how many bits long it is, what order the bytes are arranged in, and whether the most significant bit comes first or last, for example) and how many elements are in the array. The first section describes various encodings of traditional computer data, such as integers, floating-point numbers, character strings, arrays, and structures. Well-established formats also exist for multimedia data: Video, for example, is typically transmitted in one of the formats created by the Moving Picture Experts Group (MPEG), and still images are usually transmitted in Joint Photographic Experts Group (JPEG) format. The particular issues that arise in the encoding of multimedia data are discussed in the next section.

Multimedia data types require us to think about both presentation and compression. The well-known formats for the transmission and storage of audio and video deal with both these issues: making sure that what was recorded, photographed, or heard at the sender can be interpreted correctly by the receiver, and doing so in a way that does not overwhelm the network with massive amounts of multimedia data.

Compression and, more generally, the efficiency of encoding have a rich history, dating back to Shannon's pioneering work on information theory in the 1940s. In effect, there are two opposing forces at work here. In one direction, you would like as much redundancy in the data as possible so that the receiver is able to extract the right data even if errors are introduced into the message. The error detection and correcting codes we saw in an earlier chapter add redundant information to messages for exactly this purpose. In the other direction, we would like to remove as much redundancy from the data as possible so that we may encode it in as few bits as possible. It turns out the multimedia data offers a wealth of opportunities for compression because of the way our senses and brains process visual and auditory signals. We don't hear high frequencies as well as lower ones, and we don't notice fine detail as much as the bigger picture in an image, especially if the image is moving.

Compression is important to the designers of networks for many reasons, not just because we rarely find ourselves with an abundance of bandwidth everywhere in the network. For example, the way we design a compression algorithm affects our sensitivity to lost or delayed data and thus may influence the design of resource allocation mechanisms and end-to-end protocols. Conversely, if the underlying network is unable to guarantee a fixed amount of bandwidth for the duration of a videoconference, we may choose to design compression algorithms that can adapt to changing network conditions.

Finally, an important aspect of both presentation formatting and data compression is that they require the sending and receiving hosts to process every byte of data in the message. It is for this reason that presentation formatting and compression are sometimes called data manipulation functions. This is in contrast to most of the protocols we have seen up to this point, which process a message without ever looking at its contents. Because of this need to read, compute on, and write every byte of data in a message, data manipulations affect end-to-end throughput over the network. In some cases, these manipulations can be the limiting factor.

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