A Novel Scheduler for Task scheduling in Multiprocessor System using Machine Learning approach

Authors

  • Nirmala H Dept. of CSE, RNSIT, VTU, Bangalore, India
  • Girijamma HA Dept. of CSE, RNSIT, VTU, Bangalore, India

DOI:

https://doi.org/10.26438/ijcse/v7i2.140143

Keywords:

Multiprocessor scheduling, Global scheduling, Partitioned scheduling, Machine Learning

Abstract

In today’s computing world scheduling of real time task in a multiprocessor environment is very crucial. To do the scheduling, how the scheduler is implemented? what parameters are considered ? and how those parameters affect? Is also very important. Using the realistic parameters of the task the scheduling can be done and predict the resource requirement and analysis of the resource utilization factor can be done. Based on the tasks parameter it is necessary to classify them into dependent and independent, which is very important for the scheduler to assign them to the processors. For this prediction process machine learning algorithms are applied like logistic regression, decision tree, K-means and k-NN. In this paper initially classification of tasks into two categories dependent and independent is done later the same sets can be assigned to the processors for their execution.

References

[1] Mehdi Akbari, Hassan Rashidi, “A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems”, Expert Systems With Applications, Elsevier Vol.60, pp.234-248, 2016.

[2] Baruah Bipasa Chattopadhyay, Haohan Li Insik Shin, “Mixed-criticality scheduling on multiprocessors”, Vol 50, Issue 1, pp 1–4, 2014.

[3] Sanjaya K. Panda, Indrajeet Gupta, Prasanta K. Jana, “Allocation-Aware Task Scheduling for Heterogeneous Multi-Cloud System”, Procedia Computer Science, Elsevier Vol 50, pp.176-184, 2015.

[4] Yang Xin, Lingshuang Kong, Zhi Liu , Yuling Chen, Yanmiao Li, Hongliang Zhu, Mingcheng Gao,Haixia Hou, And Chunhua Wang, “Machine Learning and Deep Learning Methods for Cybersecurity”, IEEE Access, Vol.6, pp. pp.35365-35381, 2018.

[5] Panos Louridas and Christof Ebert, “Machine Learning”, IEEE Computer Society, Vol.4, Issue.11, pp.110-115, 2016.

[6] Chouhan Kumar Rath , Shashank Sekhar Suar ,Prasanti Biswal, “A Comparative Study On Dynamic Task Scheduling Algorithms”, Journal on Information Technology, Vol.71, Issue.1, pp.1-6, 2018

[7] Sri Raj Pradhan, Sital Sharma, Debanjan Konar, Kalpana Sharma, “A Comparative Study on Dynamic Scheduling of Real-Time Tasks in Multiprocessor System using Genetic Algorithms”, International Journal of Computer Applications Network Security and Communication, Vol.120, Issue.1, pp.0975-8887, 2015.

[8] Sakshi kathuria., “A Survey on Security Provided by Multi-Clouds in Cloud Computing”, IJSRNSC, Vol.6, Issue.1, pp.23-27, 2018.

[9] Pradeep K.Sharma, Vaibhav Sharma and Jagrati Nagdiya, “A proposed Method for Mining High Utility Itemset with Transactional Weighted Utility using Genetic Algorithm Technique ( -GA),” IJSRCSE, Vol.5, Issue.1, pp.31-35, 2017.

[10] Marko Bertogna, Michele Cirinei, Giuseppe Lipari Member “Schedulability analysis of global scheduling algorithms on multiprocessor platforms”, IEEE Transactions on Parallel and Distributed System,Vol X,No. X 2008.

[11] Rekha A Kulkarni ,Suhas H Patil, N.Balaji, “Fuzzy Real Time Scheduling on Distributed Systems to Meet the Deadline of Applications”, International Journal of New Technology and Research, Vol.2, Issue.4, pp.56-58, 2016.

[12] Wei Zhao and Krithi Ramamritham, “,” Simple and Integrated Heuristic Algorithms for Scheduling Tasks with Time and Resource Constraints”, Journal of Systems and Software,Vol 7, pp.195-205, 1987.

[13] Ashish Sharma and Mandeep Kaur,” An Efficient Task Scheduling of Multiprocessor Using Genetic Algorithm Based on Task Height”, JITSE, Vol 5, Issue 2 1000151, ISSN: 2165-7866,2015.

[14] Nirmala H, Girijamma H A,” Aperiodic task Scheduling Algorithms for Multiprocessor systems in Real Time environment”, International Journal of Engineering and Computer Science, ISSN 2319-7242, Vol 4, Issue 8, pp 13838-13841, 2015.

[15] Christos Gogos, Christos Valouxis, Panayiotis Alefragis , George Goulas, Nikolaos Voros ,Efthymios Housos,” Scheduling independent tasks on heterogeneous processors using heuristics and Column Pricing”, Future Generation Computer Systems, Issue 60, pp 48-66, 2016.

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Published

2019-02-28
CITATION
DOI: 10.26438/ijcse/v7i2.140143
Published: 2019-02-28

How to Cite

[1]
H. Nirmala and H. Girijamma, “A Novel Scheduler for Task scheduling in Multiprocessor System using Machine Learning approach”, Int. J. Comp. Sci. Eng., vol. 7, no. 2, pp. 141–143, Feb. 2019.

Issue

Vol. 7 No. 2 (2019): IJCSE February Edition

Section

Research Article

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