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As you can with groups, you can place a frame within a frame within a frame and so on, to create as many nested levels as you want. The same caveat applies: Keep things as simple as possible to achieve the desired effect. To nest a frame within a frame:

1.6 Normalized spectral efficiency 1.4 1.2 1 0.8 0.6 0.4 0.2 0 3.6 4.8 7.2 Available frequency spectrum (MHz) one TX antenna DD/PH on BCCH frequencies (two TX antennas) DD/PH on all frequencies (two TX antennas)

Now that the variables are properly defined, you can make the actual connection to MySQL. This is accomplished by the mysql_connect() function within PHP. This function accepts three arguments: host, username, and password defined just a moment ago.

Name and Description Step Into: Perform the current script step. If the current step is a Perform Script step, the Script Debugger will enter the sub-script, stopping at the first step in the sub-script. The Active Scripts list will be updated to indicate the nesting level, as shown in Figure 24-2. Step Out: Perform all remaining steps in the current script until either the (sub)script ends or a breakpoint is encountered. Run: Perform all script steps until the script ends, a pause condition is reached, or a breakpoint is encountered. Halt Script: Cease executing the script and exit /close the Script Debugger. Set Next Step: If you select a step in the list of script steps and click this button, the selected step will become the current step (next to be executed).

In the Target text field, specify how you want the link to be displayed:

AppLeS results, the scheduler uses static and dynamic information about resources, as well as application-level information (number of tasks, size of data les, etc) in order to make scheduling decisions Previous AppLeS work only addressed adaptive scheduling at the onset of the application, as opposed to during the execution Departing from the AppLeS work, APST targets applications that are long running and therefore it must re ne scheduling decisions throughout application execution In Reference [22], we presented an adaptive scheduling algorithm that re nes the application schedule periodically In particular, we focused on application scenarios in which potentially large input data les can be shared by several application tasks, which occurs for many PSAs It is then critical to maximize the reuse of those les This can be achieved via le replication and scheduling of computational tasks close to relevant les.

In Microsoft Word (Mac or Windows), choose Insert Symbol. (It may also be accessible through the toolbar if you added this command to your toolbar; look for the button with the character.) In Corel WordPerfect, choose Insert Symbol. (Use the Set popup menu to switch among different types of symbols.) In Apple iWork Pages, choose Edit Special Characters. (Use the list of symbol categories at left to switch among different types of symbols.)

must be equal to 1, which means the Miller Rabin test would have ended after Step MR4. Example 13.11 Miller Rabin test for N 229, N 2 1 22 57

This scheduling problem is NP-complete and we employed list scheduling heuristics with dynamic priorities [38] We hypothesized that this would be a good approach to our scheduling problem by identifying commonalities between the concept of task-host af nities, de ned in Reference [39], and the notion of closeness of data to computation We developed a new heuristic, XSufferage, and validated our scheduling approach in simulation We showed that our adaptive scheduling algorithm tolerates the kind of performance prediction errors that are expected in Grid environments We implemented our algorithm in the APST scheduler for four different heuristics We also implemented a simple greedy algorithm that uses task duplication We then compared these different scheduling approaches on a real Grid test bed in Reference [23] Deciding which scheduling algorithm is appropriate for which situation is a dif cult question.