Cuckoo Genetic Optimization Algorithm for Efficient Job Scheduling with Load Balance in Grid Computing

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Author(s)

Rachhpal Singh 1,*

1. Department of Computer Science Guru Nanak Dev University, Amritsar-Punjab, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2016.08.07

Received: 5 Jan. 2016 / Revised: 1 Apr. 2016 / Accepted: 12 May 2016 / Published: 8 Aug. 2016

Index Terms

Grid computing, Cuckoo optimization algorithm, Load Balance, Transfer time, Genetic algorithm, Make span time, Schedule length

Abstract

Grid computing incorporates dispersed resources to work out composite technical, industrial, and business troubles. Thus a capable scheduling method is necessary for obtaining the objectives of grid. The disputes of parallel computing are commencing with the computing resources for the number of jobs and intricacy, craving, resource malnourishment, load balancing and efficiency. The risk stumbling upon parallel computing is the enthusiasm to scrutinize different optimization techniques to achieve the tasks without unsafe surroundings. Here Cuckoo Genetic Optimization Algorithm (CGOA) is established that was motivated from cuckoo optimization algorithm (COA) and genetic algorithm (GA) for task scheduling in parallel environment (grid computing system). This CGOA is implemented on parallel dealing out for effective scheduling of multiple tasks with less schedule length and load balance. Here transmission time is evaluated with number of job set. This is computed with the help of job-processor relationship. This technique handles the issues well and the results show that complexity, load balance and resource utilization are finely managed.

Cite This Paper

Rachhpal Singh, "Cuckoo Genetic Optimization Algorithm for Efficient Job Scheduling with Load Balance in Grid Computing", International Journal of Computer Network and Information Security(IJCNIS), Vol.8, No.8, pp.59-66, 2016. DOI:10.5815/ijcnis.2016.08.07

Reference

[1]I. Foster, C. Kesselman, “The Grid: Blueprint for a Future Computing Infrastructure”, Morgan Kaufman Publishers, USA, 1999.
[2]I. Foster, C. Kesselman, “The Grid 2: Blueprint for a New Computing Infrastructure”, 2nd ed., Morgan Kaufmann, 2004.
[3]Ajith Abraham, Rajkumar Buyya and Baikunth Nath, "Nature’s Heuristics for Scheduling Jobs on Computational Grids", IEEE international conference on advanced computing and communications, 2000.
[4]Fatos Xhafa and Ajith Abraham, "Meta-heuristics for Grid Scheduling Problems, Springer, 2008.
[5]Masoud Yaghini and Mohammad Rahim Akhavan Kazemzadeh, "DIMMA: A Design and Implementation Methodology for Meta heuristic Algorithms - A Perspective from Software Development, International Journal of Applied Meta heuristic Computing, 1(4), 58-75, October-December 2010.
[6]Jennifer M. Schopf, "Ten actions when grid scheduling".
[7]Javier Carretero and Fatos Xhafa, "Genetic algorithm based schedulers for grid computing systems", International Journal of Innovative Computing, Information and Control ICIC International, Vol. 3, 2007
[8]Lei Zhang, Yuehui Chen, Runyuan Sun, Shan Jing and Bo Yang, "A Task Scheduling Algorithm Based on PSO for Grid Computing", International Journal of Computational Intelligence Research, Vol.4, pp. 37–43, 2008.
[9]P. Mathiyalagan, S.Suriya and Dr. S. N. Sivanandam, "Modified Ant Colony Algorithm for Grid Scheduling", International Journal on Computer Science and Engineering, Vol. 02, pp. 132-139, 2010.
[10]Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra and Prachet Bhuyan, "A Survey of Job Scheduling and Resource Management in Grid Computing", World Academy of Science, Engineering and Technology, Vol.4, 2010
[11]Pinky Rosemarry, Payal Singhal, and Ravinder Singh, "A Study of Various Job & Resource Scheduling Algorithms in Grid Computing", International Journal of Computer Science and Information Technologies, Vol. 3, pp. 5504-5507, 2012.
[12]Xin-She Yang and Suash Deb, "Cuckoo Search via L′evy Flights", IEEE Publications, pp. 210-214, 2009.
[13]Jean-Paul Watson, “Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem”, 2003.
[14]Liang Sun, Xiaochun Cheng and Yanchun Liang, "Solving Job Shop Scheduling Problem Using Genetic Algorithm with Penalty Function", International Journal of Intelligent Information Processing, Vol.1, 2010.
[15]Hedieh Sajedi and Maryam Rabiee, “A Meta heuristic Algorithm for Job Scheduling in Grid Computing”, I.J. Modern Education and Computer Science, vol.5, pp. 52-59, 2014.
[16]J.C. Beck, T.K. Feng and J.P. Watson, “A Hybrid Constraint Programming / Local Search Approach to the Job-Shop Scheduling Problem”, INFORMS Journal on Computing, Vol. 23, No. 1, pp. 1–14, 2011.
[17]Liang Sun, Xiaochun Cheng , Yanchun Liang, "Solving Job Shop Scheduling Problem Using Genetic Algorithm with Penalty Function", IJIIP: International Journal of Intelligent Information Processing, Vol. 1, No. 2, pp. 65 ~ 77, 2010
[18]Said Fathy El-Zoghdy, “A Hierarchical Load Balancing Policy for Grid Computing Environment” J. Computer Network and Information Security, 2012, 5, 1-12 Published Online June 2012 in MECS, (http://www.mecs-press.org/) DOI: 10.5815/ijcnis.2012.05.01
[19]G. Kanagaraj, S.G. Ponnambalam, N. Jawahar, “A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems”, Computers & Industrial Engineering, Volume 66, Issue 4, December 2013, Pages 1115–1124.
[20]Ritu Garg, Awadhesh Kumar Singh, “Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure”, I. J. Computer Network and Information Security, 2013, 6, 18-26, Published Online May 2013 in MECS (http://www.mecs-press.org/), DOI: 10.5815/ijcnis.2013.06.03.
[21]Saeed Molaiy, Mehdi Effatparvar, “Scheduling in Grid Systems using Ant Colony Algorithm”, I.J.Computer Network and Information Security, 2014, 2, 16-22, Published Online January 2014 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijcnis.2014.02.03
[22]Chandrashekhar Azad1, Vijay Kumar Jha, “Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System”, J. Computer Network and Information Security, 2015, 8, 56-
71, Published Online July 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijcnis.2015.08.07.