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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Review Article

Learning-Based Task Scheduling Using Big Bang Big Crunch for Cloud Computing Environment

Author(s): Pradeep Singh Rawat, Priti Dimri and Punit Gupta*

Volume 13, Issue 2, 2020

Page: [137 - 146] Pages: 10

DOI: 10.2174/2213275912666190204125712

Price: $65

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Abstract

Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm and Round Robin improve the performance but these are not cost efficient at the same time.

Scheduling issue and resource cost resolve using improved meta-heuristic approaches. In this work, a cost aware algorithm improved using Big-Bang Big-Crunch based task mapping is proposed which reduces the execution time and cost paid for the resources at the time of execution. The cost aware meta-heuristic technique used. Results show that the proposed algorithm provides better cost efficiency than the existing genetic algorithm. The proposed Big-Bang Big-Crunch based resource allocation technique evaluated against the Genetic approach. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The population size and user requests measures the performance of the proposed model.

The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost).

Keywords: ACO, Big-Bang Big Crunch (BB-BC), genetic, optimization, resource, cloud computing.

Graphical Abstract

[1]
M. Kalra, and S. Singh, "“A review of metaheuristic scheduling techniques in cloud computing.” Egypt", Informatics J., vol. 16, no. 3, pp. 275-295, 2015.
[2]
E.G. Talbi, Metaheuristics: From design to implementation.New Jersey, . Wiley, 2009.
[http://dx.doi.org/10.1002/9780470496916]
[3]
Q. Guo, "Task scheduling based on ant colony optimization in cloud environment", AIP Conference Proceedings. Vol. 1834, No.1. New York: AIP Publishing LLC, 2017.
[http://dx.doi.org/10.1063/1.4981635]
[4]
M. Dorigo, and L.M. Gambardella, "Ant colony system: A cooperative learning approach to the traveling salesman problem", IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 53-66, 1997.
[http://dx.doi.org/10.1109/4235.585892]
[5]
E. Salari, and K. Eshghi, "An ACO algorithm for graph coloring problem", In: 2005 ICSC Congress on Computational Intelligence Methods and Applications. IEEE, 2005, pp. 5.
[http://dx.doi.org/10.1109/CIMA.2005.1662331]
[6]
X. Zhang, and L. Tang, "CT-ACO-hybridizing ant colony optimization with cyclic transfer search for the vehicle routing problem", 2005 ICSC Congress on Computational Intelligence Methods and Applications. IEEE, 2005, pp. 6
[7]
O.K. Erol, and I. Eksin, “A new optimization method: Big bang–big crunch”, Advan. Eng. Soft., vol. 3. Elsevier, pp. 106-111. 2006
[http://dx.doi.org/10.1016/j.advengsoft.2005.04.005]
[8]
S. Javanmardi, M. Shojafar, D. Amendola, N. Cordeschi, H. Liu, and A. Abraham, "Hybrid job scheduling algorithm for cloud computing environment", 5th International Conference on Innovations in Bio- Inspired Computing and Application (IBICA). 2014, pp.43-52.
[http://dx.doi.org/10.1007/978-3-319-08156-4_5]
[9]
P. Kumar, and A. Verma, "Scheduling using improved genetic algorithm in cloud computing for independent tasks", International Conference on Advances in Computing Communications and Informatics. 2012, pp. 137-142.
[http://dx.doi.org/10.1145/2345396.2345420]
[10]
Y. Ge, and G. Wei, "GA-Based task scheduler for the cloud computing systems", International Conference on Web Information Systems and Mining. 2010, pp. 181-186.
[http://dx.doi.org/10.1109/WISM.2010.87]
[11]
Y. Changtian, and Y. Jiong, "Energy-aware genetic algorithms for task scheduling in cloud computing", Seventh China Grid Annual Conference. 2012, pp. 43-48.
[12]
S. Singh, and M. Kalra, "Scheduling of independent tasks in cloud computing using modified genetic algorithm", International Conference on Computational Intelligence and Communication Networks (CCIN). November 2014, pp. 565-569.
[http://dx.doi.org/10.1109/CICN.2014.128]
[13]
R. Sharma, and M. Bharti, "Mapping of tasks to resources maintaining fairness using swarm optimization in cloud environment", 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO). October 2014, pp. 1-6
[http://dx.doi.org/10.1109/ICRITO.2014.7014724]
[14]
A.H. Kashan, and B. Karimi, “A discrete particle swarm optimization algorithm for scheduling parallel machines.” Comp. Indust. Eng., Elsevier, 2009, pp. 216-223.
[15]
M. Kalra, and S. Sarbjeet, "A review of metaheuristic scheduling techniques in cloud computing", Egypt. Info. J. Elsevier,. 2015, pp. 1-21.
[16]
M.K. Goyal, A. Aggarwal, P. Gupta, and P. Kumar, "QoS based trust management model for Cloud IaaS", In: 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.. IEEE. pp. 843-847, 2012.
[http://dx.doi.org/10.1109/PDGC.2012.6449933]
[17]
C.V. Camp, "Design of space trusses using big bang–big crunch optimization", J. Struct. Eng., vol. 133, pp. 999-1008, 2007.
[http://dx.doi.org/10.1061/(ASCE)0733-9445(2007)133:7(999)]
[18]
S. Sindhu, and S. Mukherjee, "A genetic algorithm based scheduler for cloud environment", 4th International Conference on Computer and Communication Technology (ICCCT). 2013, pp. 23-27.
[http://dx.doi.org/10.1109/ICCCT.2013.6749597]
[19]
S.T. Kaveh, "Size optimization of space trusses using big bang–big crunch algorithm", Comp. Struct., Elsevier,. Vol. 87, No. 17-18, pp.1129-1140, 2009.
[http://dx.doi.org/10.1016/j.compstruc.2009.04.011]
[20]
S. Sharma, S. Kumar, and B. Singh, "Routing in wireless mesh networks: two soft computing based approaches", Int. J. Mobile Net. Comm. Telemat.(IJMNCT), vol. 3, no. 3, pp. 29-39, 2013.
[http://dx.doi.org/10.5121/ijmnct.2013.3304]
[21]
G.M. Jaradat, and M. Ayob, "Big bang-big crunch optimization algorithm to solve the course timetabling problem", 10th International Conference on Intelligent Systems Design and Applications. December 2010, pp. 1448-1452.
[http://dx.doi.org/10.1109/ISDA.2010.5687114]
[22]
O.K. Erol, and I. Eksin, "A new optimization method: Big bang-big crunch", Adv. Eng. Softw., vol. 37, no. 2, pp. 106-111, 2006.
[http://dx.doi.org/10.1016/j.advengsoft.2005.04.005]
[23]
J. Marchini, Lecture 5: The Poisson Distribution., Distribution, 2008, pp. 1-9.
[24]
C. Science, and W. Bengal, "A smart job scheduling system for cloud computing service providers and users", Model. Simul. (Anaheim). 2012
[25]
L. Guo, T. Yan, S. Zhao, and C. Jiang, "Dynamic Performance Optimization for Cloud Computing Using M / M / m", Queueing Syst., 2014.
[http://dx.doi.org/10.1155/2014/756592]

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