Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

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

Research Article

A Data Placement Strategy Based on Crow Search Algorithm in Cloud Computing

Author(s): Avinash Kaur, Pooja Gupta* and Manpreet Singh

Volume 13, Issue 1, 2020

Page: [43 - 52] Pages: 10

DOI: 10.2174/2213275912666181127123431

Price: $65

Abstract

Background: In cloud computing era, large scale scientific applications process large amount of data in the data centers. Placing of this data onto a data center is a critical issue performed as part of workflow management system and aims to find the best data center to place the data. It has a direct impact on performance, cost and execution time of workflows.

Methods: In this paper, a novel data placement strategy is proposed based on Crow Search Algorithm (CSA) that dynamically distributes the data sets to appropriate data centers during runtime stage of workflow.

Results: The results obtained b y CSA based run time data placement algorithm are evaluated with the results of other algorithms. Simulation results acknowledge that using CSA based data placement algorithm provides appropriate results in comparison to the other algorithms.

Keywords: Data placement, cloud computing, scientific workflow, crow search algorithm, directed acyclic graph, particle swarm optimization.

Graphical Abstract

[1]
E. Deelman, and A. Chervenak, "Data management challenges of data-intensive scientific workflows", In: Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID). Lyon, France, 2008, pp. 687-692.
[http://dx.doi.org/10.1109/CCGRID.2008.24]
[2]
A. Labrinidis, and H.V. Jagadish, "Challenges and opportunities with big data", In Proceedings VLDB Endowment---, vol. 5, no. 12, pp. 2032-2033, 2012.
[http://dx.doi.org/10.14778/2367502.2367572]
[3]
J.J. Rehr, F.D. Vila, J.P. Gardner, L. Svec, and M. Prange, "Scientific computing in the cloud", Comput. Sci. Eng., vol. 12, no. 03, pp. 34-43, 2010.
[http://dx.doi.org/10.1109/MCSE.2010.70]
[4]
A. Saxena, G. Shrivastava, and K. Sharma, "Forensic investigation in cloud computing environment", IJOFCS, vol. 7, no. 2, pp. 64-74, 2012.
[http://dx.doi.org/10.5769/J201202005]
[5]
H.N. Wang, W.X. Xu, and C.L. Jia, "A high speed railway data placement strategy based on cloud computing", Appl. Mech. Mater., vol. 135, pp. 43-49, 2012.
[http://dx.doi.org/10.1155/2012/396387]
[6]
K. Sharma, and B.B. Gupta, "Multi-layer defense against malware attacks on smartphone wi-fi access channel", Procedia Comput. Sci., vol. 78, no. C, pp. 19-25, 2016.
[http://dx.doi.org/10.1016/j.procs.2016.02.005]
[7]
S.K. Shrivastava, P. Kumar, and A. Pandey, "Impact of Software Licenses in Cloud Computing Based E-Governance Initiatives", In: Proceedings of the Fourth International Conference on Communication Systems and Network Technologies. Bhopal, India, 2014, pp. 592-596
[http://dx.doi.org/10.1109/10.1109/CSNT.2014.125]
[8]
G. Shrivastava, K. Sharma, and A. Bawankan, "A new framework semantic web technology based e-learning", In: Proceedings of the 11th International Conference on Environment and Electrical Engineering. Venice, Italy, 2012, pp. 1017-1021.
[http://dx.doi.org/10.1109/EEEIC.2012.6221527]
[9]
C. Blum, and A. Roli, "Metaheuristics in combinatorial optimization: Overview and conceptual comparison", ACM Comput. Surv., vol. 35, no. 3, pp. 268-308, 2003.
[10]
X.S. Yang, Metaheuristic optimization. Scholarpedia, Vol. 6, No. 8, 2011.,
[http://dx.doi.org/10.4249/scholarpedia.11472]
[11]
J. Holland, Adaptation in natural and artificial systems: An introductory analysis with application to biology, control and artificial intelligence., MIT Press, 1992.
[12]
J. Kennedy, and R. Eberhart, "Particle swarm optimization", In: Proceedings of IEEE International Conference on Neural Networks IV.Perth, WA, Australia 1995, pp. 1942-1948, Vol. 4.
[13]
Z.W. Geem, J.H. Kim, and G.V. Loganathan, "A new heuristic optimization algorithm: Harmony search", Simulation, vol. 76, no. 2, pp. 60-68, 2001.
[14]
X.S. Yang, and S. Deb, "Cuckoo search via lévy flights", In: IEEE World Congress on Nature and Biologically Inspired Computing.Coimbatore, India 2009, pp. 210-214.
[15]
M. Khari, and P. Kumar, "An effective meta-heuristic cuckoo search algorithm for test suite optimization", Informatica, vol. 41, no. 3, pp. 363-377, 2017.
[16]
S. He, Q.H. Wu, and J. Saunders, "Group search optimizer: An optimization algorithm inspired by animal searching behavior", InIEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 973-990, 2009.
[17]
X.S. Yang, "A new metaheuristic bat-inspired algorithm In Nature Inspired Cooperative Strategies for Optimization, Berlin, Germany,", 2010, pp. 65-74.
[18]
X.S. Yang, "Firefly algorithm, stochastic test functions and design optimisation", Int. J. Bio-inspired Comput., vol. 2, no. 2, pp. 78-84, 2010.
[19]
L. Guo, Z. He, S. Zhao, N. Zhang, J. Wang, and C. Jiang, "Multi-objective optimization for data placement strategy in cloud computing", In: International Conference on Information Computing and Applications.Chengde, China 2012, pp. 119-126
[20]
J. Myint, and T.T. Naing, "A data placement algorithm with binary weighted tree on PC cluster based cloud storage system", In: IEEE International Conference on Cloud and Service Computing. Hong Kong, 2011, pp. 315-320
[21]
X. Liu, and A. Datta, "Towards intelligent data placement for scientific workflows in collaborative cloud environment", In: IEEE International Symposium on Parallel and Distributed Processing Workshops and Ph.D Forum, Shanghai, China, 2011, pp. 1052-1061.
[http://dx.doi.org/10.1109/IPDPS.2011.259]
[22]
J.K. Wang, and X. Jia, "Data security and authentication in hybrid cloud computing model", In: Global High Tech Congress on Electronics.Shenzhen, China 2012, pp. 117-120.
[http://dx.doi.org/10.1109/GHTCE.2012.6490136]
[23]
D. Yuan, Y. Yang, X. Liu, and J. Chen, "A data placement strategy in scientific cloud workflows, future generation", Comput. Syst., vol. 26, no. 8, pp. 1200-1214, 2010.
[24]
Z. Er-Dun, Q. Yong-Qiang, X. Xing-Xing, and C. Yi, "A data placement strategy based on genetic algorithm for scientific workflows", In: Eighth International Conference on Computational Intelligence and Security (CIS).Guangzhou, China 2012, pp. 146- 149.
[25]
W. Guo, and X. Wang, "A data placement strategy based on genetic algorithm in cloud computing platform", In: 10th Web Information System and Application Conference.Yangzhou, China 2013, pp. 369-372.
[26]
T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms., Oxford University Press, 1996.
[27]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multi objective genetic algorithm: Nsgaii", IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, 2002.
[28]
M. Mitchell, An Introduction to Genetic Algorithms., MIT Press, 1998.
[29]
P. Zheng, L.Z. Cui, H.Y. Wang, and M. Xu, "A data placement strategy for data-intensive applications in cloud", Jisuanji Xuebao, vol. 33, no. 8, pp. 1472-1480, 2010.
[http://dx.doi.org/10.3724/SP.J.1016.2010.01472]
[30]
L. Guo, S. Zhao, S. Shen, and C. Jiang, "Task scheduling optimization in cloud computing based on heuristic algorithm", J. Netw., vol. 7, no. 3, pp. 547-553, 2012.
[31]
J.M. Cope, N. Trebon, H.M. Tufo, and P. Beckman, "Robust data placement in urgent computing environments", In: IEEE International Symposium on Parallel & Distributed Processing.Rome, Italy 2009, pp. 1-13
[http://dx.doi.org/10.1109/IPDPS.2009.5160914]
[32]
S. Pandey, L. Wu, S.M. Guru, and R. Buyya, "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments", In: 24th IEEE International Conference on Advanced Information Networking and Applications.Perth, WA, Australia 2010, pp. 400-407.
[33]
Q. Zhao, C. Xiong, and P. Wang, "Heuristic data placement for data-intensive applications in heterogeneous cloud", J. Electr. Comput. Eng., vol. 13, pp. 1-8, 2016.
[34]
J. Yan, Y. Yang, and G.K. Raikundalia, "SwinDeW-a p2p-based decentralized workflow management system", IEEE Trans. Syst. Man Cybern. A Syst. Hum., vol. 36, no. 5, pp. 922-935, 2006.
[http://dx.doi.org/10.1109/TSMCA.2005.855789]
[35]
https://en.wikipedia.org/wiki/Corvus [Accessed on: Nov 02, 2018]
[36]
A.D. Craig, "How do you feel-now? The anterior insula and human awareness", Nat. Rev. Neurosci., vol. 10, no. 1, pp. 59-70, 2009.
[37]
D.C. Penn, and D.J. Povinelli, "On the lack of evidence that non-human animals possess anything remotely resembling a ‘Theory of mind’", Philos. Trans. R. Soc. Lond. B Biol. Sci., vol. 362, no. 1480, pp. 731-744, 2007.
[38]
S. Venugopal, and R. Buyya, "An scp-based heuristic approach for scheduling distributed data-intensive applications on global grids", J. Parallel Distrib. Comput., vol. 68, no. 4, pp. 471-487, 2008.
[http://dx.doi.org/10.1016/j.jpdc.2007.07.004]
[39]
Y. Yang, K. Liu, J. Chen, X. Liu, D. Yuan, and H. Jin, "An Algorithm in SwinDeW-C for Scheduling Transaction-Intensive Cost-Constrained cloud workflows", In: IEEE Fourth International Conference on eScience.Indianapolis, IN, USA 2008, pp. 374-375.
[40]
A. Askarzadeh, "A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm", Comput. Struc., vol. 169, pp. 1-2, 2016.
[41]
Y. Yang, K. Liu, J. Chen, J. Lignier, and H. Jin, "Peer- to-peer based grid workflow runtime environment of SwinDeW-G", In: 3rd IEEE International Conference on e-Science and Grid Computing,. Bangalore, India, 2007, pp. 51-58.
[http://dx.doi.org/10.1109/E-SCIENCE.2007.56]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy