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Once you have optimized the traffic over your internetwork by applying access lists to all appropriate types of traffic and periodic updates, you can further improve the efficiency of WAN links by data compression, a cost-effective way to improve the throughput available on a given link If you have a 64 kbps link that is approaching saturation point, your choices are to upgrade to a 128 kbps link or to see if you can get more out of the existing link by data compression All data compression schemes work on the basis of two devices connected on one link, both running the same algorithm that compresses the data on the transmitting device and decompresses the data on the receiving device Conceptually, two types of data compression can be applied, depending on the type of data being transported These two types of data are lossy and lossless At first, a method called lossy a name that implies it will lose data might not seem particularly attractive There are types of data, however, such as voice or video transmission, that are still usable despite a certain amount of lost data Allowing some lost data significantly increases the factor by which data can be compressed JPEG and MPEG are examples of lossy data compression In our case, though, we are building networks that need to deliver data to computers that typically do not accept any significant data loss Lossless data compression comes either as statistical or dictionary form The statistical method is not particularly applicable here, as it relies on
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the traffic that is being compressed to be consistent and predictable, when internetwork traffic tends to be neither Cisco s data compression methods, STAC and Predictor, are based on the dictionary style of compression These methods rely on the two communicating devices sharing a common dictionary that maps special codes to actual traffic patterns STAC is based on the Lempel-Ziv algorithm that identifies commonly transmitted sequences and replaces those sequences in the data stream with a smaller code This code is then recognized at the receiving end, extracted from the data stream, and the original sequence inserted in the data stream In this manner, less data is sent over the WAN link even as transmission of the same raw data is permitted Predictor tries to predict the next sequence of characters, based upon a statistical analysis of what was transmitted previously My experience has led me to use the STAC algorithm, which, although it is more CPU-intensive than Predictor, requires less memory to operate No matter which method you choose, you can expect an increase in latency Compression algorithms delay the passage of data through an internetwork While typically this is not very significant, some client/server applications that are sensitive to timing issues may be disrupted by the operation of data compression algorithms One of the great marketing hypes of the networking world has been the advertisement of impressive compression rates from vendors offering data compression technology To its credit, Cisco has always advertised the limitations of compression technology as well as its potential benefits First of all, there is no such thing as a definitive value for the compression rate you will get on your internetwork Compression rates are totally dependent on the type of traffic being transmitted If your traffic is mainly ASCII text with 70 to 80 percent data redundancy, you may get a compression ratio near 4:1 A typical target for internetworks that carry a mix of traffic is more realistically 2:1 When you implement data compression, you must keep a close eye on the processor and memory utilization The commands to monitor these statistics are show proc and show proc mem, respectively Implementing Data Compression The first data compression technique to which most people are introduced is header compression, as implemented by the Van Jacobson algorithm This type of compression can deliver benefits when the traffic transported consists of many small packets, such as Telnet traffic The processing requirements for this type of compression are high, and it is therefore rarely implemented on links with greater throughput than 64 kbps The most popular implementation of Van Jacobson header compression is for asynchronous links, where it is implemented on a per-interface basis Many popular PC PPP stacks that drive asynchronous communication implement this type of header compression as default The following enables Van Jacobson header compression on interface Async 1, where the passive keyword suppresses compression until a compressed header has been received from the computer connecting to this port Router1(config)#interface async 1 Router1(config-int)#ip tcp header-compression passive
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Clearly, Van Jacobson header compression works only for IP traffic Better overall compression ratios can be achieved by compressing the whole data stream coming out of an interface by using
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the actual user data and therefore will not be readable by the router receiving the compressed data stream A clear disadvantage to this type of compression is that, if traffic has to traverse many routers from source to destination, potentially significant increases in latency may occur That can happen because traffic is compressed and decompressed at every router through which the traffic passes This is necessary for the receiving router to read the uncompressed header information and decide where to forward the packet Per-interface compression delivers typical compression rates of 2:1 and is therefore worth serious consideration for internetworks with fewer than 10 routers between any source and destination This type of compression can be implemented for point- to-point protocols such as PPP or the default Cisco HDLC The following example shows implementation for PPP on the Serial 0 interface For this method to work, both serial ports connected on the point-to-point link must be similarly configured for compression Router1(config)#interface serial 0 Router1(config-int)#encapsulation ppp Router1(config-int)#compress stac The per-interface type of compression that requires each router to decompress the received packet before it can be forwarded is not applicable for use on a public network such as frame relay or X25 The devices within the public network probably will not be configured to decompress the packets in order to determine the header information needed to forward packets within the public network For connection to public networks, Cisco supports compression on a per-virtual-circuit basis This type of compression leaves the header information intact and compresses only the user data being sent An example of implementing this type of configuration for an X25 network is shown as follows As with per-interface compression, both serial ports that are connected (this time via a public network), must be similarly configured Router1(config)#interface serial 0 Router1(config-int)#x25 map compressedtcp 193111 1234567879 compress This command implements TCP header compression with the compressedtcp keyword Pervirtual-circuit compression is implemented via the compress keyword at the end of the command Great care should be taken when you are considering implementing compression on a per-virtualcircuit basis Each virtual circuit needs its own dictionary, which quickly uses up most of the available memory in a router My happiest experiences with compression have been with perinterface compression on networks with a maximum hop count of around 6 or 7 from any source to any destination
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