Demand in any period that is outside the limits established by management policy.
Performing "what-if" analysis on the internals of source code, which we can't change or even access, does not make sense. For the Netscape Enterprise Web Server model, we started with the web sample application provided with Strategizer, and made a number of modifications.
For all of the commercially available products, we like to work with the vendors and other companies using Strategizer to better characterize these applications. Creating Model Behaviors Our methodology proposes that developers participate in creating Strategizer component models of the application programs, for which they are currently responsible.
A major milestone in determining the complete adoption of Strategizer modeling activities by the Wells developer community will depend, in part, on their acceptance of Strategizer's ADN simulation language. As of this writing, the jury is still out on them picking up this task.
As expected, developers are somewhat resistant to developing in other than their primary programming language. The authors appreciate the value added by the ADN language.
For a modeling specialist, it's a perfectly reasonable to exclusively use a simpler language tailored to simulation tasks to get results fast. However, our methodology specifically decrees that developers not be "molded" into modeling specialists. The development of sophisticated cross-language compiler technology is clearly beyond our project scope.
As a minimum, there seems to be agreement at Wells that the tasks of building and periodically updating ADN-based modeling components from source code can be precisely defined and scoped and therefore easily and economically out-sourced as an alternative.
Again, the goal of the methodology is to reduce the degree of expert knowledge in building these application models through the use of procedures and "wizard-like" tools.
In writing behaviors for the model in ADN, we chose to use variable names, branching conditions and other code constructs, which closely paralleled those found in the actual source code.
We used indenting of ADN statements in Strategizer behavior editing to indicate the function-calling hierarchy in the actual application source code instead of the block structure in the ADN code.
Some of the comments contained identifiers or variables for function execution times as previously measured using the Quantify application profiler discussed below.
Quantify pairs these same function identifier names with the functions' execution times when it creates two separate output files. One Quantify output file contains execution times for just the function calls. Another Quantify output file contains the execution times for the functions added to the times for all of the descendent function calls of those functions.
Customer was used if just the execution time to call a Customer object constructor function was desired. Customer was used if the execution times for the Customer object constructor function and all of the descendent functions that it called were desired.
For this, we wrote a Perl script, which also parsed the Quantify files containing the execution times associated with those same identifier names. As output, the Perl script created another Strategizer Include file containing Constant assignments equal to the sum of execution times for a given block of F: When we began the modeling, we used, for the most part, Quantify execution times of the FD: Later, as we added more application depth to the model, which we learned from examining lower level function definitions, we replaced the top FD: Our objective is to automate the task of entering the large number of execution times.
Whenever we have to re-profiling the many Internet Banking programs with Quantify because of coding changes or ports to other platforms, it's a simple matter to re-run the Perl script once afterwards to update the execution times in the Strategizer Include file that the Perl script generates.
With fifty or more execution times in the model, this saves a lot of time and typing mistakes. The authors concede that the ADN programming conventions described above make the Strategizer behavior code more difficult to read for the uninitiated.
However, our primary goal is to make the ADN behaviors easier for developers of the application programs to read, validate, update execution parameters and hopefully take ownership.
Since the developers are already users of Quantify in their test environment we feel that application profiling is actually an easier process for getting execution times as compared to setting up a benchmarking environment, measuring response times and inferring execution times.
We avoided this pitfall by keeping in mind that we were only interested in a few threads of execution for the model. However, ADN variable names with underscores instead of ".
These high-level documents, published on the Wells intranet, proved very useful to other performance analysts in our group for interpreting performance logging data. In some cases we provided hypertext links from function calls described in our narrative to the appearance of the function names in tracing logs generated by actually running the application with debugging turned on.
This was done to make it easier for others to validate the accuracy of our documentation. From these documents we wrote the ADN behavior code for just the threads of execution that we wanted to model - the transactions with high levels of visibility within the bank.
Strategizer's Documentation Generator facility, which creates excellent HTML-formatted documentation of Strategizer behaviors, was a highly valued addition to our Wells intranet documentation capabilities. From our high-level application narrative Web pages, we created hypertext links from application function names described in the narrative to the HTML tags for associated model behaviors that were created by Strategizer's Document Generator.
ADN proved to be robust enough for the translation from actual production source code. An extra Receive parameter was sometimes used to indicate source file descriptor information for the select call. However, one particularly difficult modeling task was encountered where a master process blocked on a select function call until a socket message was received.
The complication arose because the master process then used a UNIX kill statement to signal one of a pool of worker processes whose signal handlers did the actual read from one of the file descriptors contained in the read mask of the select function.Software life cycle models describe phases of the software cycle and the order in which those phases are executed.
Each phase produces deliverables required by the next phase in the life cycle. The Seven Phases of the System-Development Life Cycle. the SDLC uses a systems approach to describe a process.
It is often used and followed when there is an IT or IS project under development. The SDLC highlights different stages (phrases or steps) of the development process.
network engineer and/or database developer are brought on to. The database development life cycle. Free statement of participation on completion. More about this course. Course description Course content Course reviews Populating the database.
Supporting users and user processes. Supporting data management strategies. Testing. Maintenance. Conclusion. View CIS Week 6 Discussion from CIS at Strayer University, Washington.
"Database Life Cycle" Please respond to the following: Per the text, the Database Life Cycle includes the Database%(28). System Study. Preliminary system study is the first stage of system development life cycle. This is a brief investigation of the system under consideration and gives a clear picture of what actually the physical system is?
Requirements Analysis is the first and most important stage in the Database Life Cycle. It is the most labor-intensive for the database designer. This stage involves assessing the informational needs of an organization so that a database can be designed to meet those needs.