Optimization of System’s Performance with Kernel Tracing by Cohort Intelligence

Full Text (PDF, 816KB), PP.59-66

Views: 0 Downloads: 0

Author(s)

Aniket B. Tate 1,* Laxmi A. Bewoor 1

1. Dept. of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, 411048, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2017.06.08

Received: 24 Aug. 2016 / Revised: 20 Dec. 2016 / Accepted: 5 Mar. 2017 / Published: 8 Jun. 2017

Index Terms

Kernel Trace, Linux Tracing Tool Next Generation (LTTng), Metaheuristics, Cohort Intelligence

Abstract

Linux tracing tools are used to record the events running in the background on the system. But these tools lack to analyze the log data. In the field of Artificial Intelligence Cohort Intelligence (CI) is recently proposed technique, which works on the principle of self-learning within a cohort. This paper presents an approach to optimize the performance of the system by tracing the system, then extract the information from trace data and pass it to cohort intelligence algorithm. The output of cohort intelligence algorithm shows, how the load of the system should be balanced to optimize the performance.

Cite This Paper

Aniket B. Tate, Laxmi A. Bewoor, "Optimization of System's Performance with Kernel Tracing by Cohort Intelligence", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.6, pp.59-66, 2017. DOI:10.5815/ijitcs.2017.06.08

Reference

[1]G. Anderson, T. Marwala, and F. V. Nelwamondo, “Use of Data Mining in Scheduler Optimization,” p. 10, 2010.

[2]T. A. Maktum, R. A. Dhumal, and L. Ragha, “A Genetic Approach for Processor Scheduling,” in IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, 2014, pp. 9–12.

[3]A. Kaur and B. S. Khehra, “CPU Task Scheduling using Genetic Algorithm,” in IEEE 3rd International Conference onMOOCs, Innovation and Technology in Education (MITE), 2015, no. 2003, pp. 66–71.

[4]Chiang, Y.-C. Lee, C.-N. Lee, and T.-Y. Chou, “Ant colony optimisation for task matching and scheduling,” Comput. Digit. Tech. IEEE Proc., vol. 153, no. 2, pp. 130–136, 2006.

[5]K. Kotecha and A. Shah, “Adaptive scheduling algorithm for real-time operating system,” in IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008, pp. 2109–2112.

[6]A. J. Kulkarni, I. P. Durugkar, and M. Kumar, “Cohort intelligence: A self supervised learning behavior,” in Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2013, pp. 1396–1400.

[7]A. J. Kulkarni and H. Shabir, “Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm,” Int. J. Mach. Learn. Cybern., no. Ci, p. 15, 2014.

[8]N. Bhulli, “ktrace_dissertation.pdf.”

[9]C. LaRosa, L. Xiong, and K. Mandelberg, “Frequent pattern mining for kernel trace data,” in Proceedings of the 2008 ACM symposium on Applied computing - SAC ’08, 2008, p. 880.

[10]S. Punhani, Akash Sumit, Kumar Rama, Chaudhary Avinash Kumar, “A Cpu scheduling based on multi criteria with the help of Evolutionary Algorithm,” in 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, 2012, pp. 730–734.

[11]A. Yasin, A. Faraz, and S. Rehman, “Prioritized Fair Round Robin Algorithm with Variable Time Quantum,” in 13th International Conference on Frontiers of Information Technology, 2015, pp. 314–319.

[12]Ravindra A. Vyas, H. H. Maheta, V. K. Dabhi, and H. B. Prajapati, “Load balancing using process migration for linux based distributed system,” in Internationai Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 248–252.

[13]G. Vallee, R. Lottiaux, D. Margery, C. Morin, and J. Y. Berthou, “Ghost process: A sound basis to implement process duplication, migration and checkpoint/restart in linux clusters,” in 4th International Symposium on Parallel and Distributed Computing, ISPDC, 2005, vol. 2005, pp. 97–104.

[14]A. Zarrabi, K. Samsudin, and A. Ziaei, “Dynamic process migration framework,” in International Conference of Information and Communication Technology, ICoICT, 2013, pp. 410–415.

[15]B. A. Mustafa, N. T. Saleh, and A. M. Khidhir, “Process Migration Based on Memory to Memory Mechanism,” in The First International Conference of Electrical, Communication, Computer, Power and Control Engineering ICECCPCE, 2013, p. 5. 

[16]Ahmed F. Ali, “Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems,” I.J. Information Engineering and Electronic Business (IJIEEB),2014, vol.6, no.3, pp. 51-63.

[17]Hedieh Sajedi, Maryam Rabiee, “A Metaheuristic Algorithm for Job Scheduling in Grid Computing,” I.J. Modern Education and Computer Science (IJMECS),2014, vol.6, no.5, pp. 52-59.