Abstract
The rapid growth in the field of cloud computing leads to migration of people to cloud, which makes them feel more tensile and adaptive in the environment. Cloud makes the users give less importance to maintain their hardware and other resources because all these works are done by a service provider. This would restrict the users from spending more money on capital expenditure and so the people are ready to invest more in the cloud. The virtual machine (VM) is a buzz which replaced a traditional physical machine through the method called virtualization. Virtualization is the main objective for establishing cloud services. The core idea of virtualization is to create an instance or virtual machine according to user demands, and the number of servers needed is proportional to the amount need for the resource pool. This paper presents a heuristic algorithm—gravitational search algorithm (GSA)—which formulate an optimal solution for allocating task on the cloud. To analysis the efficiency of the proposed algorithm, a comparative study has been done with other heuristic algorithms like the ant colony and particle swarm optimization which is also used for job allocation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu C, Han J, Shang Y, Liu C, Cheng B, Chen J (2017) Predicting of job failure in compute cloud based on online extreme learning machine: a comparative study. IEEE Trans J 5:1011–1025
Gulati A, Chopra RK (2014) Dynamic round Robin for load balancing in a cloud computing. IJCSMC 2:274–278
Mao C, Lin R, Xu C, He R (2017) Towards a trust prediction framework for cloud services based on PSO-driven neural networks. IEEE Trans J 79:88–115
Gautam P, Bansal R (2014) Extended round Robin load balancing cloud computing. IJEC 3:27–39
Farrag AAS, Mahmoud SA, El Sayed M, EI-Horbaty (2015) IEEE Conference on Intelligent Cloud Algorithms for Load Balancing problems: A Survey, pp 231–242, vol 3
Zarrabi A, Samsudin K (2014) Task scheduling on computational grids using gravitational search algorithm. J Clust Comput 17(3):1001–1011. ACM
Rastkhadiv F, Zamanifar K (2016) Task scheduling based on load balancing using artificial bee colony in cloud computing environment. IJBR 7(Special Issue-No 5):1058–1069
Ghanbari S, Othman M (2012) A priority based job scheduling algorithm in cloud computing 03(01):778–785. Elsevier conference
Zhong Z, Chen K, Zhai X, Zhou S (2016) Virtual machine-based task scheduling algorithm in a cloud computing environment. IEEE 21(6):660–667
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: gravitational search algorithm. Inf Sci 179(13):2232–2248. Elsevier
Liu X-F, Zhan Z-H, Deng JD, Li Y, Gu T, Zhang J (2018) Ant colony optimization for VM to savings of energy and efficient use of different resources. IEEE Trans 22(1)
de Moura Oliveira PB, Oliveira J, Cunha JB (2017) Trends in gravitational search algorithm. In: 14th international conference on distributed computing and artificial intelligence, DCAI 2017. Advances in intelligent systems and computing, vol 620. Springer, Berlin
Liu L, Qiu Z (2016) A survey on VM scheduling in cloud computing. In: 2016 2nd IEEE international conference in computer and communications (ICCC)
Zhang W-Z, Xie H-C, Hsu C-H (2017) Automatic memory control of multiple virtual machines on a consolidated server. IEEE Trans Cloud Comput 5(1):2–14
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Manasa, M., Priyadarshini, J. (2019). Job Allocation on Cloud: A Comparative Study. In: Nath, V., Mandal, J. (eds) Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-13-7091-5_35
Download citation
DOI: https://doi.org/10.1007/978-981-13-7091-5_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7090-8
Online ISBN: 978-981-13-7091-5
eBook Packages: EngineeringEngineering (R0)