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

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Research Article

Task Scheduling in Cloud Using ACO

Author(s): Yuvaraj Natarajan*, Srihari Kannan and Gaurav Dhiman

Volume 15, Issue 3, 2022

Published on: 31 August, 2020

Article ID: e180322185405 Pages: 6

DOI: 10.2174/2666255813999200831112705

Price: $65

Abstract

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment.

Introduction: Certain problems such as inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies.

Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling that has been proposed using Ant Colony Optimization (ACO). These approaches enable the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment.

Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay.

Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

Keywords: Load balancing, task scheduling, virtualization, cloud computing, ACO.

Graphical Abstract

[1]
J.W. Rittinghouse, and J.F. Ransome, "Cloud computing: Implementation, management and security".CRC Press, 2017.
[2]
A. Botta, W. De Donato, V. Persico, and A. Pescapé, "Integration of cloud computing and Internet of Things: A survey", Future Gener. Comput. Syst., vol. 56, pp. 684-700, Mar 2016.
[http://dx.doi.org/10.1016/j.future.2015.09.021]
[3]
C. Yang, Q. Huang, Z. Li, K. Liu, and F. Hu, "Big data and cloud computing: Innovation opportunities and challenges", Int. J. Digit. Earth, vol. 10, no. 1, pp. 13-53, Jan 2017.
[http://dx.doi.org/10.1080/17538947.2016.1239771]
[4]
S. K. Mishra, B. Sahoo, and P. P. Parida, "Load balancing in cloud computing: A big picture", J. King Saud Univ.-Comput. Inf. Sci., 2018.
[5]
D.C. Devi, and V.R. Uthariaraj, "Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks", ScientificWorldJournal, vol. 2016, p. 14, 2016.
[http://dx.doi.org/10.1155/2016/3896065] [PMID: 26955656]
[6]
M. Mesbahi, and A.M. Rahmani, "Load balancing in cloud computing: A state of the art survey", Int. J. Mod. Educ. Comput. Sci., vol. 8, no. 3, p. 64, Mar 2016.
[http://dx.doi.org/10.5815/ijmecs.2016.03.08]
[7]
S. Dam, G. Mandal, K. Dasgupta, and P. Dutta, "An ant-colony-based meta-heuristic approach for load balancing in cloud computing", In: Applied Computational Intelligence and Soft Computing in Engineering., IGI Global, 2018, pp. 204-232.
[http://dx.doi.org/10.4018/978-1-5225-3129-6.ch009]
[8]
S.A. Naqvi, N. Javaid, H. Butt, M.B. Kamal, A. Hamza, and M. Kashif, "Metaheuristic optimization technique for load balancing in cloud-fog environment integrated with smart grid", International Conference on Network-Based Information Systems, 2018pp. 700-711
[9]
R. Bhole, H.J. Singh, P. Khamkar, P. Joshi, and R. Bendbhar, "Load balancing in cloud computing using autonomous agents", Imp. J. Interdiscip. Res, vol. 3, no. 3, pp. 237-239, Mar 2017.
[10]
A. Pradhan, S.K. Bisoy, and P.K. Mallick, Load balancing in cloud computing: Survey.Innovation in Electrical Power Engineering, Communication, and Computing Technology., Springer: Singapore, 2020, pp. 99-111.
[http://dx.doi.org/10.1007/978-981-15-2305-2_8]
[11]
V.N. Volkova, L.V. Chemenkaya, E.N. Desyatirikova, M. Hajali, A. Khodar, and A. Osama, "Load balancing in cloud computing", In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2018, pp. 387-390.
[http://dx.doi.org/10.1109/EIConRus.2018.8317113]
[12]
A. Gupta, and R. Garg, "Load balancing based task scheduling with ACO in cloud computing", In: International Conference on Computer and Applications (ICCA), 2017, pp. 174-179.
[http://dx.doi.org/10.1109/COMAPP.2017.8079781]
[13]
A. Mehta, A. Kaur, and P. Singh, "A heuristic approach for efficient load balancing in cloud using weight based algorithm", In: 4th International Conference on Computing Sciences (ICCS), 2018, pp. 1-6.
[http://dx.doi.org/10.1109/ICCS.2018.00007]
[14]
S. Sethi, A. Sahu, and S.K. Jena, "Efficient load balancing in cloud computing using fuzzy logic", IOSR J. Eng., vol. 2, no. 7, pp. 65-71, July 2012.
[http://dx.doi.org/10.9790/3021-02716571]
[15]
A. Tripathi, S. Shukla, and D. Arora, "A hybrid optimization approach for load balancing in cloud computing".Advances in Computer and Computational Sciences., Springer: Singapore, 2018, pp. 197-206.
[http://dx.doi.org/10.1007/978-981-10-3773-3_19]
[16]
M. Krishnan, S. Yun, and Y.M. Jung, "Enhanced clustering and ACO-based multiple mobile sinks for efficiency improvement of wireless sensor networks", Comput. Netw., vol. 160, pp. 33-40, 2019.
[http://dx.doi.org/10.1016/j.comnet.2019.05.019]
[17]
L. Eskandari, A. Jafarian, P. Rahimloo, and D. Baleanu, "A modified and enhanced ant colony optimization algorithm for traveling salesman problem"Mathematical Methods in Engineering, Springer: Cham, 2019, pp. 257-265.
[http://dx.doi.org/10.1007/978-3-319-91065-9_13]

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