ARTIFICIAL FLOOR PLAN GENERATION USING MACHINE LEARNING, CONFPROFITT: A PERFORMANCE PROFILING TESTING
Abstract
Sadly, builders often have no idea how the overall performance of a device suffers from configuration variables and the way they interact. The frequency of configuration errors inducing overall performance concerns has been studied in advance research. According to Han et al, configuration issues account for 59% of performance issues. Displays a performance flaw in Apache as a result of the configuration. When one enters a high value for the configuration parameter start servers (for example, 60), Apache restarts more slowly than usual. A valuable method called dummy connection contained inside a for loop is the number one culprit in this problem. This dummy connection technology initiates Apache Baby Server strategies through calling device features such as pick and ballot. To overcome this mistake, an if clause is added to the for loop. ConfPro prefers the white-field method to black-container performance profiling so that builders can choose configuration-dependent, inefficient code locations.
Keywords
Artificial, Machine LearningHow to Cite
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