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where P is any appropriate power norm that we choose. The least squares methods use P = 2, or an L2 norm. The angular brackets denote a sum over all of the data in the time series. An example time series is a spiral curve generated by (X, Y, Z) = (sin(t), cos(t), t), with t = [0, 10p] in increments of p/50. The time evolution of this curve appears in Figure 6.19. Note that for this problem, computation of the cost function requires a summation over 500 time increments. However, even with the reasonably large population size and number of generations (70) that we computed, the computer time required was not excessive. (Note that for a bigger problem with a longer averaging period, this would no longer be true.) A continuous GA is applied to this curve with a population size of Npop = 100, and a mutation rate of m = 0.2. Since the GA is oblivious to which value of P we choose, we experimented a bit and found the best results for moderate P. The solution displayed here uses P = 4. Evolution of the minimum cost appears in Figure 6.20. We notice that the cost decreases several orders of magnitude over the 70 generations. The result appears in Figure 6.21. We see that the general shape of the spiral curve is captured rather well. The bounds in X and Y are approximately correct, but the evolution in Z = t is too slow. We found

Remember that the cost of maintaining a site never goes away. For example, in its calculations of the cost to enter a market, EMC gures out what it costs to translate the core content catalog introduced in 3. Furthermore, it assumes a monthly rate of change on that content as well as expected changes in what makes up that catalog. Factoring these modi cations into the cost to translate, EMC then budgets a quarterly amount to maintain and update the content for each market. Of course, other kinds of applications will have different bene t schemes. For example, you would measure your supply chain s bene t in terms of pro t and inventory or asset turnovers. Factoring in other variables for ROI calculations. The problem with the cost basis approach even in its more evolved form is that it does not account for broader economic factors such as risk, actual losses, and the cost of capital. Economists have proposed more thorough formulas,1 such as gROI = income - costs - expected loss - (cost of equity economic capital) This model factors in all costs and losses but suffers from the same problem as the cost-basis model: It does not capture the continuing cost of maintenance. The following model adds that element: gROI = income - cost to develop - cost to maintain - expected loss (cost of equity economic capital) Losses include explicit fraud and customer default, the cost of dealing with shipping errors, and promotional tricks such as free or reduced cost shipping. Less easily quanti ed variables include the loss of more pro table customers over time, the cost of providing service, the effect on pro tability of tactics such as upselling, and integration with other distribution channels.

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We are also grateful for the support from colleagues and sponsors of our research on patterns and the ACE and TAO middleware frameworks, notably the contributions of Ron Akers (Motorola), Al Aho (Lucent), Steve Bachinsky (SAIC), Detlef Becker (Siemens), Jim Blaine (Washington University), John Buttitto (Motorola), Becky Callison (Boeing), Wei Chiang (Nokia), Russ Claus (NASA), Joe Cross (Lockheed Martin), Bryan Doerr (Boeing), Karlheinz Dorn (Siemens), Sylvester Fernandez (Lockheed Martin), Andreas Geisler (Siemens), Helen Gill (DARPA), Trey Grubbs (Raytheon), Jody Hagins (ATD), Andy Harvey (Cisco), Thomas Heimke (Siemens), Kalai Kalaichelvan (Nortel), Arvind Kaushal (Motorola), Steve Kay (Tellabs), Chandra Kintala (Lucent), Gary Koob (DARPA), Sean Landis (Motorola), Rick Lett (Sprint), Joe Loyall (BBN), Mike Masters (NSWC), Ed Mays (US Marine Corps), John Mellby (Raytheon), Dave Meyer (Virtual Technology), Eileen Miller (Lucent), Stan Moyer (Telcordia), Russ Noseworthy (Object Sciences), Guru Parulkar (Cisco), Dan Paulish (Siemens), James Plamondon (Microsoft), Dieter Quehl (Siemens), Lucie Robillard (US Air Force), Allyn Romanow (Cisco), Rick Schantz (BBN), Steve Shaffer (Kodak), Dave Sharp (Boeing), Naval Sodha (Ericsson), Brian Stacey (Nortel), Paul Stephenson (Ericsson), Umar Syyid (Hughes), Dave Thomas (OTI), Lothar Werzinger (Krones), Shalini Yajnik (Lucent), and Tom Ziomek (Motorola). Very special thanks go to Steve Rickaby, our copy editor, for enhancing our written material. In addition, we thank our editor, Gaynor Redvers-Mutton, and everyone else at John Wiley & Sons who made it possible to publish this book. This is the second book fostered by Gaynor and Steve. Their support has been superb and we look forward to working with them on forthcoming POSA volumes. Finally, we would like to express our deepest gratitude to the late Richard Stevens, whose seminal books inspired us to explore the wonders of network programming so many years ago. His spirit pervades this book.

for ( TInt k = 1; k <= 2000; ++k ) { // Set memory allocation to fail after k allocs __UHEAP_SETFAIL( RHeap::EDeterministic, k ); // Heap marker for detecting memory leaks __UHEAP_MARK; // Execute the test function TRAPD( err, DoTestL() ); // Check for memory leaks __UHEAP_MARKEND; if ( !err ) { // The test function did not leave // If it ran successfully, // we have found an upper bound for k in for this test function break; } }

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