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Clearly, there need to be 100 000 distinct forwarding entries for the 100 000 distinct destinations in the forwarding table How are they represented One option is to create 100 000 distinct forwarding states, one for each IP prefix When the LSP switches to the protection path, all 100 000 entries must be updated A more efficient option is to share the actual forwarding state between all IP prefixes by introducing a level of indirection For example, rather than maintaining the exact label and interface, maintain an abstract entity representing the LSP Even if the LSP switches to the protection path, from the BGP routes point of view forwarding has not changed and the BGP forwarding entries need not be modified By using indirection, fewer forwarding state updates must be made The issue of updating forwarding state for IP routes falls at the boundary between local-protection and routing protocol implementation.

It's easy to see why most observers mistakenly treat program trading as a cause of stock market trends. It provides an easy explanation and eliminates the need to dig deeper for more adequate reasons. Consider how program trading looks to the casual observer. As stock index futures rise sharply, arbitrage activity leads traders to buy a basket of stocks and sell stock index futures in order to bring the futures and cash prices of a stock index back into line. A strong upsurge in stock index futures causes "buy programs" to kick in and is considered bullish for stocks. A sharp drop in stock index futures has the opposite effect. When the drop in stock index futures goes too far, traders sell a basket of stocks and buy the stock index futures. The resulting "sell programs" pull stock prices lower and are considered to be bearish for stocks. It appears on the surface (and is usually reported) that the stock market rose (or fell) because of the program buying (or selling). As is so often the case, however, the quick and easy answer is seldom the right answer. Unfortunately, market observers see "program trading" impacting on the stock market and treat it as an isolated, marketmoving force. What they fail to realize is that the moves in stock index futures, which activate the program trading in the first place, are themselves usually caused by moves in related markets the bond market, the dollar, and commodities. And this is where the real story lies.

As we discussed earlier in the section titled Understanding the Application Design, we will be implementing the following two scenarios for the ACME Web services provider:

12

Understanding the design options provides an insight.

double rate_; double T_; double price_; double vol_; double dividend_; char type_; char exercise_; Handle<PricingEngine> engine_; OptionGreeks og; StatUtility util; MatrixUtil mu; // // // // // // // // // // //

i 11 12 13 14 15

n 2 v vmarket ((a(Ti 1 t ) + d)e b( Ti 1 t i ) ) + c )2 market i (t ) = min min (13.50) a ,b , c , d , a ,b , c , d , vmarket vmarket i =1 i =1

15.5 Krishna et al. (1976) presented a condenser design problem in which the condenser area requirements are estimated for condensing 50% of a vapor mixture containing propane(l), rc-butane(2), rc-hexane(3), ft-heptane(4), rc-octane(5), and hydrogen(6). Repeat their calculations using the design algorithms outlined in 15. This example should provide a strong reason for taking multicomponent diffusion interactions into account in design calculations. 15.6 Develop a mathematical model of an adiabatic wetted wall column. Hint: The model will be similar to that presented in Section 15.2. Use your model to generate composition profiles for the wetted wall column of Modine as discussed in Example 11.5.3. This is a lengthy exercise and will require you to calculate physical properties as a function of temperature, pressure, and composition. Modine's experimental data are most accessible in a paper by Krishna (1981a).