Open Access   Article Go Back

Modern Approaches To Cloud Scheduling

A. Upadhyay1 , R. Thakur2 , A. Thakur3

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 1072-1079, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.10721079

Online published on Jun 30, 2018

Copyright © A. Upadhyay, R. Thakur, A. Thakur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: A. Upadhyay, R. Thakur, A. Thakur , “Modern Approaches To Cloud Scheduling,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1072-1079, 2018.

MLA Style Citation: A. Upadhyay, R. Thakur, A. Thakur "Modern Approaches To Cloud Scheduling." International Journal of Computer Sciences and Engineering 6.6 (2018): 1072-1079.

APA Style Citation: A. Upadhyay, R. Thakur, A. Thakur , (2018). Modern Approaches To Cloud Scheduling. International Journal of Computer Sciences and Engineering, 6(6), 1072-1079.

BibTex Style Citation:
@article{Upadhyay_2018,
author = {A. Upadhyay, R. Thakur, A. Thakur },
title = {Modern Approaches To Cloud Scheduling},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {1072-1079},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2302},
doi = {https://doi.org/10.26438/ijcse/v6i6.10721079}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.10721079}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2302
TI - Modern Approaches To Cloud Scheduling
T2 - International Journal of Computer Sciences and Engineering
AU - A. Upadhyay, R. Thakur, A. Thakur
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 1072-1079
IS - 6
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
389 259 downloads 192 downloads
  
  
           

Abstract

Many important real world problems are computationally “hard” and one of those is cloud scheduling. There are various approaches to cloud scheduling, but in recent time scheduling strategies based on heuristics and metaheuristics are gaining popularity because of their performance. Especially important metaheuristics are nature inspired metaheuristics which have been proved to be very efficient in solving hard problems. These metaheuristics are inspired by natural phenomenon and simulate them in an algorithmic manner. In this paper, we describe those methods and present their successful applications in cloud scheduling problem. We will also describe the formal statement of the problem so that a reader can directly correlate the algorithms with applications below.

Key-Words / Index Term

Nature inspired algorithms; Optimization; Genetic algorithm; Cuckoo search optimization; Particle swarm optimization; Ant colony optimization; Cloud scheduling problem

References

[1] Hopcroft, J.E., Motwani, R. and Ullman, J.D., 2001. Introduction to automata theory, languages, and computation. ACM SIGACT News, 32(1), pp.60-65.
[2] Benatallah, B. ed., 2011. Cloud computing: methodology, systems, and applications. CRC Press.
[3] Garey, M.R. and Johnson, D.S., 1979. A Guide to the Theory of NP-Completeness. WH Freemann, New York, 70.
[4] Yu, J., Buyya, R. and Ramamohanarao, K., 2008. Workflow scheduling algorithms for grid computing. In Metaheuristics for scheduling in distributed computing environments (pp. 173-214). Springer Berlin Heidelberg.
[5] Yang, X.S., 2014. Nature-inspired optimization algorithms. Elsevier.
[6] Rahman, M., Li, X. and Palit, H., 2011, May. Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment. In Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on (pp. 966-974). IEEE.
[7] Elsayed, K.M. and Khattab, A.K., 2006. Channel-aware earliest deadline due fair scheduling for wireless multimedia networks. Wireless Personal Communications, 38(2), pp.233-252.




[8] Schuetz, H.J. and Kolisch, R., 2012. Approximate dynamic programming for capacity allocation in the service industry. European Journal of Operational Research, 218(1), pp.239-250.
[9] Shi, D. and Chen, T., 2013. Optimal periodic scheduling of sensor networks: A branch and bound approach. Systems & Control Letters, 62(9), pp.732-738.
[10] Gendreau, M. and Potvin, J.Y., 2010. Handbook of metaheuristics (Vol. 2). New York: Springer.
[11] Holland, J.H., 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
[12] Kennedy, J. and Eberhart, R.C., 1995. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Piscataway, NJ, USA; 1995. p. 1942–48.
[13] Dorigo, M., 1992. Optimization, learning and natural algorithms. Ph.
D. Thesis, Politecnico di Milano, Italy.
[14] Dorigo, M. and Maniezzo, V., Colorni (1991) A positive feedback as a search strategy. Technical report 91-016, Politecnico di Milano,
Italy.
[15] Dorigo, M., Maniezzo, V. and Colorni, A., 1996. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), pp.29-41.
[16] Gu, J., Hu, J., Zhao, T. and Sun, G., 2012. A new resource scheduling strategy based on genetic algorithm in cloud computing environment. Journal of Computers, 7(1), pp.42-52.
[17] Zhao, C., Zhang, S., Liu, Q., Xie, J. and Hu, J., 2009, September. Independent tasks scheduling based on genetic algorithm in cloud computing. In Wireless Communications, Networking and Mobile Computing, 2009. WiCom`09. 5th International Conference on (pp. 1-4). IEEE.
[18] Zhu, K., Song, H., Liu, L., Gao, J. and Cheng, G., 2011, December. Hybrid genetic algorithm for cloud computing applications. In Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific (pp. 182-187). IEEE.
[19] Zuo, L., Shu, L., Dong, S., Zhu, C. and Hara, T., 2015. A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access, 3, pp.2687-2699.
[20] X. Lu and Z. Gu, “A load-adapative cloud resource scheduling model based on ant colony algorithm,” IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 296–300, 2011.
[21] Liu, H., Xu, D. and Miao, H.K., 2011, December. Ant colony optimization based service flow scheduling with various QoS requirements in cloud computing. In Software and Network Engineering (SSNE), 2011 First ACIS International Symposium on (pp. 53-58). IEEE.
[22] Li, K., Xu, G., Zhao, G., Dong, Y. and Wang, D., 2011, August. Cloud task scheduling based on load balancing ant colony optimization. In Chinagrid Conference (ChinaGrid), 2011 Sixth Annual (pp. 3-9). IEEE.
[23] Zuo, X., Zhang, G. and Tan, W., 2014. Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Transactions on Automation Science and Engineering, 11(2), pp.564-573.
[24] Pandey, S., Wu, L., Guru, S.M. and Buyya, R., 2010, April. A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Advanced information networking and applications (AINA), 2010 24th IEEE international conference on (pp. 400-407). IEEE.
[25] Awad, A.I., El-Hefnawy, N.A. and Abdel_kader, H.M., 2015. Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Computer Science, 65, pp.920-929.
[26] Wang, Z., Shuang, K., Yang, L. and Yang, F., 2012. Energy-aware and revenue-enhancing Combinatorial Scheduling in Virtualized of Cloud Datacenter. Journal of Convergence Information Technology, 7(1), pp.62-70.
[27] Raju, R., Babukarthik, R.G., Chandramohan, D., Dhavachelvan, P. and Vengattaraman, T., 2013, February. Minimizing the makespan using Hybrid algorithm for cloud computing. In Advance Computing Conference (IACC), 2013 IEEE 3rd International (pp. 957-962). IEEE.
[28] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.