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Indeed, he=she is very likely to download a set of Web pages from different Web sites or to initiate multiple FTP downloads Therefore, equating a user session that includes Web browsing with a TCP connection is questionable Indeed, with the growth of the Internet, a person using the Internet is more likely to initiate more than one operation during a session (independent of the kind of application FTP, TELNET, or Web) This breaks the Poisson paradigm and suggests a new look at the burstiness of the TCP connection arrival process In fact, we show that WEB connection arrivals show self-similar behavior This change is an illustration of how the characteristics of the Internet can change over time While it is still true that users arrive in a more or less memoryless fashion, this does not translate into TCP connections arriving in a memoryless way Feldmann et al.

class FileApplication implements FileReaderClient { // Fragments private String[] filenames; private int currentCompletion; // index of ready file { public synchronized void readCompleted(String fn, byte[] d) // wait until ready to process this callback while (!fnequals(filenames[currentCompletion])) { try { wait(); } catch(InterruptedException ex) { return; } } // process data // wake up any other thread waiting on this condition: ++currentCompletion; notifyAll(); } public synchronized void readFailed(String fn, IOException e){ // similar } public synchronized void readfiles() { currentCompletion = 0; for (int i = 0; i < filenameslength; ++i) readerread(filenames[i],this);

[16] show that Cox's model is still applicable but should be applied to a higher level abstraction of a user session Feldmann et al [16] explain that the connection arrival process is self-similar using Cox's model by showing that the number of TCP connections initiated by a user session (in this case a modem call) is heavy-tailed 1523 Distribution of TCP Connection Interarrival Times.

In this chapter I have discussed techniques that will help you to improve the performance of your software. However, all the tweaks and tricks in the world are no substitute for a good, ef cient design. In a deeply objectoriented environment it is easy to lose sight of what is really happening to your processor and memory. I hope that this chapter will have made the underlying processes a little more familiar to you. Techniques such as unrolling and attening recursive routines into iterative ones can improve the performance of an algorithm, but if that algorithm is inherently suboptimal, it s better to avoid it. A little care during the design phase of your project can save a lot of scraping for cycles later on. Symbian OS mobile phones are getting faster and faster every year, and there is no end to this scaling in sight. The software stacks that run on them are getting larger and more powerful, and users are expecting more and more from their phones. EKA2 was designed to provide capabilities for the next generation of devices, smaller, faster and cheaper than ever. In the rich, real-time environment it provides, performance is more important than ever. Every cycle consumes a few nanojoules, and every nanojoule has to come out of a battery that is more compact than the one tted to the last model. Cycles are an endangered species; treat them like the precious commodity they are.

TCP connection interarrival times are derived from the TCP arrival process by considering the distribution of the times between consecutive arrivals of TCP connections. The fact that the TCP connection arrival process is self-similar indicates that distributions with heavy tails such as Weibull, Pareto, and lognormal distributions may yield better models for TCP connection interarrival times than exponential models. We augment previous results with statistical evidence that this is indeed the case. We show that the Weibull distribution yields a good t for the connection interarrival times of Web connections over time periods of an hour or two. Deng [11], independent of our results, also found that the Weibull distribution gives a good t. Indeed, the Weibull distribution gives a good t not just for Web connections but for all connections of a speci c type (such as FTP and TELNET) that arrived within a time period of an hour, two hours, or even days. The same holds true even if we include connections of all applications, when calculating the interarrival times over a speci c time period, or if we consider all connections in a given trace. While the Weibull distribution gives a good t when the set of connections is enlarged to include more connections, it also gives a good t if the set of connections is reduced.