Abstract
Cloud computing is the service that enables us to use computing resources such as processing entities, storage, and applications as on-demand over the web. It begins to influence many areas, e.g., government, finance, telecommunications, and education. Cloudlet scheduling is a major issue which is greatly influencing the performance of cloud computing environment. The user requests are given to datacenter broker and data center broker allotted user requests to suitable VM with the assistance of cloudlet allocation policy. So, cloudlet allocation policy must be sufficient to execute user request on VM as early as possible because several users wait to execute their request for accessing cloud services. The main aim is to use the resources effectively and get maximum profit. This paper demonstrates review of an existing cloudlet allocation policy that assists in the allocation of cloudlets on the suitable virtual machines (VMs). It utilizes all offered resources effectively and upgrades the QoS. Cloudlet allocation policy uses CloudSim Toolkit-3.0.3 for their implementation by only changing the desired classes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Foster, I., et al.: Cloud computing and grid computing 360-degree compared in grid computing environments workshop. In: GCE’08. IEEE (2008)
Ahmed, M., et al.: An advanced survey on cloud computing and state-of-the-art research issues. Int. J. Comput. Sci. Issues (IJCSI) (2012)
Sindhu, S.: Task scheduling in cloud computing. Int. J. Adv. Res. Comput. Eng. Technol. 46, 3019–3023 (2016)
Lei, X., Zhe, X., Shao Wu, M., Xiong Yan, T.: Cloud computing and services platform construction of telecom operator. In: Broadband Network & Multimedia Technology, IC-BNMT’09. 2nd IEEE International Conference on Digital Object Identifier, pp. 864–867 (2009)
Geetha, V., et al.: Performance comparison of cloudlet scheduling policies. In: International Conference on Emerging Trends in Engineering, Technology and Science (CENTERS). IEEE (2016)
Etminani, K., Naghibzadeh, M.: A min-min max-min selective algorithm for grid task scheduling. In The Third IEEE/IFIP International Conference on Internet, Uzbekistan (2007)
Banerjee, S., et al.: An approach toward amelioration of a new cloudlet allocation strategy using cloudsim. Arab. J. Sci. Eng. 1–24 (2017)
Banerjee, S., et al.: Development and analysis of a new cloudlet allocation strategy for QoS improvement in the cloud. Arab. J. Sci. Eng. 40(5), 1409–1425 (2015)
Parsa, S., Reza E.-M.: RASA: a new task scheduling algorithm in a grid environment. World Appl. Sci. J. (Special issue of Computer & IT) 7, 152–160 (2009)
Roy, S., et al.: Development and analysis of a three-phase cloudlet allocation algorithm. J. King Saud Univ.-Comput. Inf. Sci. (2016)
Al Warafi, M.A., et al.: An improved SJF scheduling algorithm in cloud computing environment. In 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT). IEEE (2016)
Chatterjee, T., et al.: Design and implementation of an improved datacenter broker policy to improve the QoS of a cloud. In: Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Springer, Cham (2014)
Wickremasinghe, B., Calheiros, R.N., Buyya, R.: Cloud analyst: a cloudsim-based visual modeler for analyzing cloud computing environments and applications. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 446–452. IEEE (2010)
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
Anuragi, R., Pandey, M. (2019). Review Paper on Cloudlet Allocation Policy. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_29
Download citation
DOI: https://doi.org/10.1007/978-981-13-1951-8_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1950-1
Online ISBN: 978-981-13-1951-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)