Application of Data Mining Techniques in the Linux Processes
Linux processes provide all application information, auxiliary programs, and batches (such as network services and daemons). Many processes are demands to scheduling without prior knowledge of the system health problem, as response time and performance is critical to good management. This work proposes a study to explore data mining techniques using clustering, anomaly detection, attribute selection and classification algorithms, applied to the mass of information kept by the system kernel to prove informations about process behaviour to help process scheduling.
Who is your audience? Peoples interessing in the Linux, process scheduling and data mining.