Abstract
Cloud computing is one of the strongest paradigms in computing. The advancement of cloud storage infrastructure renders things complicated due to the huge growth within the field. The load balancing from a virtual computer plays a significant role in effective planning. The natural solution named “healthy bee feed” (LB-HBF), a system for the automated load balancing of machinery, is suggested here. The job and machine assignment method is the same time assignment in the context of this LB-HBF. This strategy assigns virtual machines activities primarily and reduces the relocation of work through virtual equipment. Load balancing is considered one of the key mechanisms for the efficient distribution of cloud services. The issue of load balancing would in future involve entirely autonomous distributed networks. In this case, an Osmosis Load Balancing (OLB) technique has been implemented. In order to program functions on Virtual Computers, OLB works on an osmosis principle. The method is based on the Digital Hash Table (DHT) chord overlay scheme. The overlay of chord is used to track the state of clouds and organic workers. In a variety of heterogeneous and homogeneous clouds, the proposed algorithms demonstrate better efficiency by simulation experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Naha RK, Othman M (2014) Brokering and load-balancing mechanism in the cloud—revisited. IETE Tech Rev 31(4):271–276
Fang Y, Wang F, Ge J (2010) A task scheduling algorithm based on load balancing in cloud computing. In: Web information systems and mining. Springer, pp 271–277
Maguluri ST, Srikant R, Ying L (2012) Stochastic models of load balancing and scheduling in cloud computing clusters. In: INFOCOM 2012 proceedings IEEE, pp. 702–710
Randles M, Lamb D, Taleb-Bendiab A (2010) A comparative study into distributed load balancing algorithms for cloud computing. In: 2010 IEEE 24th international conference on advanced information networking and applications workshops (WAINA), pp 551–556
Lucas-Simarro JL, Moreno-Vozmediano R, Montero RS, Llorente IM (2013) Scheduling strategies for optimal service deployment across multiple clouds. Future Generat Comput Syst 29:1431–1441
Wickremasinghe B, Calheiros RN, Buyya R (2010) Cloudanalyst: a cloudsim-based visual modeller for analysing cloud computing environments and applications. In 2010 24th IEEE international conference advanced information networking and applications (AINA), pp 446–452
[online] Available https://newsroom.fb.com/Key-Facts
Hu J, Gu J, Sun G, Zhao T (2010) A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In: Parallel architectures algorithms and programming (PAAP) 2010 third international symposium, pp 89–96
Naha RK, Othman M (2014) Evaluation of cloud brokering algorithms in cloud based data center. In: International computer science and engineering conference (ICSEC), pp 78–82
Mahajan K, Makroo A, Dahiya D (2013) Round Robin with server affinity: A VM load balancing algorithm for cloud based infrastructure. J Info Process Syst 9:379–394
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software Pract Experi 41:23–50
Domanal SG, Reddy GRM (2014) Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: 2014 sixth international conference on communication systems and networks (COMSNETS), pp 1–4
Wang SC, Yan K-Q, Liao W-P, Wang S-S (2010) Towards a load balancing in a three-level cloud computing network. In: 2010 3rd IEEE international conference on computer science and information technology (ICCSIT), pp 108–113
Venkata Krishna P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13:2292–2303
Florence AP, Shanthi V (2014) A load balancing model using firefly algorithm in cloud computing. J Comput Sci 10(7):1156–1165
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lakshmi, S.N., Paul, M., Nagaprasad, S. (2021). A Nature-Inspired Solution to Managing Activities in the Cloud with Equal Time by Using Machine Learning Approach. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_20
Download citation
DOI: https://doi.org/10.1007/978-981-15-8767-2_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8766-5
Online ISBN: 978-981-15-8767-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)