I.J. Information Technology and Computer Science, 2017, 6, 59-66 Published Online June 2017 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2017.06.08 Optimization of System’s Performance with Kernel Tracing by Cohort Intelligence Aniket B. Tate Dept. of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, 411048, India E-mail:
[email protected] Laxmi A. Bewoor Dept. of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, 411048, India E-mail:
[email protected] Abstract—Linux tracing tools are used to record the execution time etc. which shows better results. While events running in the background on the system. But scheduling a process on one of the cores, the scheduler these tools lack to analyze the log data. In the field of considers the average waiting time, turnaround time, time Artificial Intelligence Cohort Intelligence (CI) is recently quantum for a process, number of context switches, proposed technique, which works on the principle of self- earliness, the tardiness of process etc. But the scheduler learning within a cohort. This paper presents an approach does not take CPU load into consideration. As a result of to optimize the performance of the system by tracing the this, the cores get unevenly loaded and many of the cores system, then extract the information from trace data and will be kept in ideal state. Cohort Intelligence (CI) is pass it to cohort intelligence algorithm. The output of recently introduced meta-heuristics [6][7] it works on cohort intelligence algorithm shows, how the load of the self-supervised learning behavior in a cohort.