Cloud Scheduling using Meta Heuristic Algorithms
DOI:
https://doi.org/10.26438/ijcse/v5i10.132139Keywords:
Cloud Computing, Task Scheduling, Meta-heuristic, hyper heuristic, PSO, GA, ACOAbstract
Cloud computing has transformed into a well-known in area of high performance, cloud computing as it offers on-request access to shared pool of resources over web in a self-service, dynamically scalable. One of the important research issues which need to be focused for its efficient performance on task scheduling which plays the key role for increase the efficiency of whole cloud computing facilities. implies that to assign best suitable resources for the requested task to be execute with the various parameters like time, cost, scalability, makespan, reliability, resource utilization, accessibility, throughput etc. In this paper, we give survey and relative studies of a few task scheduling using metaheuristic algorithms for cloud computing.
References
R. Buyya, C. S. Yeoa, S. Venugopal, J. Broberg, I. Brandic,
“Cloud computing and emerging IT platforms: Vision, hype,
and reality for delivering computing as the 5th utility”, Future
Generation Computer Systems the International Journal of
eScience, 2009.
S. Kumar, R. H. Goudar, “Cloud Computing – Research Issues,
Challenges, Architecture, Platforms and Applications: A
Survey”, International Journal of Future Computer and
Communication, Vol. 1, No. 4, December 2012.
R. Nallakumar, N. Sengottaiyan, S. Nithya “A Survey of Task
Scheduling Methods in Cloud Computing” International
Journal of Computer Sciences and Engineering (IJCSE), Vol.
, No. 10. Oct 2014.
H. Chen. Professor Frank Wang, Dr N. Helian, G. Akanmu,
“User-Priority Guided Min-Min Scheduling Algorithm For
Load Balancing in Cloud Computing”, Parallel Computing
Technologies (PARCOMPTECH), National Conference, Feb
S. Devipriya, C. Ramesh, “Improved Max-Min Heuristic Model
For Task Scheduling In Cloud”, in Green Computing,
Communication and Conservation of Energy (ICGCE),
International Conference, Dec. 2013.
M. Kalra, S. Singh, “A review of metaheuristic scheduling
techniques in cloud computing” Egyptian Informatics Journal,
Vol. 16, Issue 3, pp. 275–295 Nov 2015.
F. Pop, C. Dobre, V. Cristea “Genetic algorithm for DAG
scheduling in grid environments” Intelligent Computer
Communication and Processing, IEEE 5th International
Conference, Aug 2009.
J. Gu J. Hu, T. Zhao, G. Sun, “A New Resource Scheduling
Strategy Based on Genetic Algorithm in Cloud Computing
Environment”, Journal of Computers, Vol. 7, No.1, Jan 2012.
K. Zhu, H. Song, L. Liu, J. Gao, G. Cheng “Hybrid Genetic
Algorithm for Cloud Computing Applications”, in Services
Computing Conference (APSCC), IEEE Asia-Pacific, Dec.
Z. Zheng ,R. Wang, H. Zhong, X. Zhang, “An Approach for
Cloud Resource Scheduling Based on Parallel Genetic
Algorithm”, in Computer Research and Development
(ICCRD), 3rd International Conference, Mar 2011.
K. Dasguptaa, B. Mandalb, P. Duttac, J. K. Mondald, S. Dame,
"A Genetic Algorithm (GA) based Load Balancing Strategy for
Cloud Computing", in International Conference on
Computational Intelligence: Modeling Techniques and
Applications (CIMTA) 2013.
M. Shojafar, S. Javanmardi, S. Abolfazli, N. Cordeschi, “A
joint meta-heuristic approach to cloud job scheduling
algorithm using fuzzy theory and a genetic method”, Cluster
Computing, Vol. 18, Issue 2, pp 829–844, June 2015.
K. Kaur , A. Chhabra , G Singh, “Heuristics based genetic
algorithm for scheduling static tasks in homogeneous parallel
system”. International Research Journal of Computer Science
(IRJCS), vol. 2, issue 9, pp. 14-19, Sep 2015.
M. Dorigo and T.Stützle, Ant Colony Optimization, The MIT
Press Cambridge, 2004.
M. A. Tawfeek, A. E.Sisi, A. E. keshk, F. A. Torkey, “Cloud
task scheduling based on ant colony optimization”, Computer
Engineering & Systems (ICCES), 8th International Conference,
Jan 2013.
P. Mathiyalagan, S. Suriya, Dr. S. N. Sivanandam, “Modified
Ant Colony Algorithm for GridScheduling”, International
Journal on Computer Science and Engineering (IJCSE), Vol.
, No. 02, pp. 132-139, 2010.
W. N. Chen, J. Zhang, Y. Yu, "Workflow scheduling in grids:
an ant colony optimization approach", Evolutionary
Computation, CEC, IEEE , Sep 2007.
J. Bagherzadeh,M. MadadyarAdeh, "An improved ant
algorithm for grid scheduling problem", in 14th International
CSI Computer Conference, CSICC, Oct 2009.
K. Li, G. Xu, G. Zhao, Y. Dong, D. Wang, “Cloud Task
scheduling based on Load Balancing Ant Colony
Optimization” Sixth Annual Chinagrid Conference (ChinaGrid)
Conference IEEE, Aug 2011.
J. Kennedy, R. Eberhart, “Particle Swarm Optimization”, in
IEEE International Conference on Neural Networks, Dec 1995.
H. Liu,A. Abraham, A. E. Hassanien, “Scheduling jobs on
computational grids using a fuzzy particle swarm optimization
algorithm”, Future Generation Computer Systems, Vol. 26,
Issue 8, pp. 1336–1343, Oct 2010.
L Zhang, Y. Chen, B. Yang, “Task Scheduling Based on PSO
Algorithm in Computational Grid”, Sixth International
Conference on Intelligent Systems Design and Applications,
Oct 2006.
Z. Pooranian, M. Shojafar, J. H. Abawajy, A. Abraham, “An
efficient meta-heuristic algorithm for grid computing”, Journal
of Combinatorial Optimization, Vol 30, Issue 3, pp 413–434,
Oct 2015.
X.S. Yang, “A New Metaheuristic Bat-Inspired Algorithm”,
Nature Inspired Cooperative Strategies for Optimization of the
series Studies in Computational Intelligence(NICSO) , Vol.
, pp 65-74, Apr 2010.
L. Jacob, “Bat algorithm for resource scheduling in cloud
computing”, International Journal for Research in Applied
Science and Engineering Technology (IJRASET), Vol 2, Issue
, Apr 2014.
S Raghavan, P. Sarwesh, C. Marimuthu, K. Chandrasekaran,
“Bat Algorithm for Scheduling Workflow Applications in
Cloud”, Electronic Design, International Conference on
Computer Networks & Automated Verification (EDCAV), Jan
S. Joshi, S. Kour, "Cuckoo search Approach for Virtual
Machine Consolidation in Cloud Data Centre", in International
Conference on Computing, Communication and Automation
(ICCCA), May 2015.
X. Wen, M. Huang, J. Shi, "Study on Resources Scheduling
Based on ACO Algorithm and PSO Algorithm in Cloud
Computing", 11th International Symposium on Distributed
Computing and Applications to Business, Engineering &
Science, Oct 2012.
Dr. S. George, “Hybrid PSO-MOBA for Profit Maximization in
Cloud Computing”, International Journal of Advanced
Computer Science and Applications (IJACSA), Vol. 6, No. 2,
pp 159-163, 2015.
G. S. Sadasivam, D. Selvaraj, “A Novel Parallel Hybrid PSO-
GA using MapReduce to Schedule Jobs in Hadoop Data
Grids”, Second World Congress on Nature and Biologically
Inspired Computing (NaBIC) IEEE, Dec. 2010.
R. Raju, R.G. Babukarthik, D. Chandramohan, P.
Dhavachelvan, T. Vengattaraman, “Minimizing the Makespan
using Hybrid Algorithm for Cloud Computing”, 3rd
International Advance Computing Conference (IACC) IEEE,
Feb 2013.[32] M. Gendreau, Handbook of Metaheuristics, Second ed., vol.
, New York Dordrecht Heidelberg London: Springer, 2010,
pp. 449-468.
C.W. Tsai, W. C. Huang, M. H. Chiang, M. C. Chiang, C.S.
Yang, "A Hyper-Heuristic Scheduling Algorithm for Cloud",
IEEE Transactions on Cloud Computing, Vol. 2, No. 2, pp
-250, April-June 2014.
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