Abstract
This review paper provides the reader with an overview of some of the many resource scheduling algorithms. The paper also describes the characteristics of these algorithms and highlights their strengths and weaknesses. The main focus is on comparing and evaluating different resource scheduling algorithms so that one can incorporate them as required. The paper also discusses potential directions for future study in the field of resource scheduling in virtual environments. A conclusion is drawn upon careful analysis, comparison, and assessment of various algorithms and their applicability for use in practical applications.
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
Similar content being viewed by others
References
Ghafir SM, Alam A, Siddiqui F, Naaz S (2021) Virtual machine allocation policy for load balancing. J Phys Conf Ser 2070(1). https://doi.org/10.1088/1742-6596/2070/1/012129
Elmagzoub MA, Syed D, Shaikh A, Islam N, Alghamdi A, Rizwan S (2021) A survey of swarm intelligence based load balancing techniques in cloud computing environment. Electronics (Switzerland) 10(21), MDPI. https://doi.org/10.3390/electronics10212718
Abhinav Chand N, Hemanth Kumar A, Teja Marella S (2018) Cloud computing based on the load balancing algorithm. Int J Eng Technol 7(4.7):131. https://doi.org/10.14419/ijet.v7i4.7.20528
Alam M, Khan ZA (2017) Issues and challenges of load balancing algorithm in cloud computing environment. Indian J Sci Technol 10:974–6846. https://doi.org/10.17485/ijst/2017/v10i25/105688
Chaudhary D, Singh R, Tech CM, Head S (2013) A new load balancing technique for virtual machine cloud computing environment
Chawla I (2018) Cloud computing environment: a review. Int J Comput Technol 17(2):7261–7272. https://doi.org/10.24297/ijct.v17i2.7674
Mayur S, Chaudhary N (2019) Enhanced weighted round robin load balancing algorithm in cloud computing. Int J Innov Technol Exploring Eng 8(9S2):148–151. https://doi.org/10.35940/ijitee.I1030.0789S219
Sidhu A, Kinger S (2005) Analysis of load balancing techniques in cloud computing. Int J Comput Technol 4(2):737–741. https://doi.org/10.24297/ijct.v4i2C2.4194
Yang X-S (2010) A new metaheuristic bat-inspired algorithm, pp 65–74. https://doi.org/10.1007/978-3-642-12538-6_6
Sharma S, Kr. Luhach A, Sheik Abdhullah S (2016) An optimal load balancing technique for cloud computing environment using bat algorithm. Indian J Sci Technol 9(28). https://doi.org/10.17485/ijst/2016/v9i28/98384
Islam T, Islam ME, Ruhin MR (2018) An analysis of foraging and echolocation behavior of swarm intelligence algorithms in optimization: ACO, BCO and BA. Int J Intell Sci 08(01):1–27. https://doi.org/10.4236/ijis.2018.81001
Vijaya V, Pentapalli G, Kiran Varma R (2007) IJARCCE cuckoo search optimization and its applications: a review. Int J Adv Res Comput Commun Eng ISO 3297(11). https://doi.org/10.17148/IJARCCE.2016.511119
Xu P, He G, Li Z, Zhang Z (2018) An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization. Int J Distrib Sens Netw 14(12). https://doi.org/10.1177/1550147718793799
Rajab H, Kabalan K (2016) A dynamic load balancing algorithm for computational grid using ant colony optimization. Indian J Sci Technol 9(21). https://doi.org/10.17485/ijst/2016/v9i21/90840
Suryadevera S, Chourasia J, Rathore S, Jhummarwala A (2014) Load balancing in computational grids using ant colony optimization algorithm. Int J Comput Commun Technol 262–265. https://doi.org/10.47893/ijcct.2014.1255
Liu Z, Qiu X, Zhang N (2021) ACPEC: a resource management scheme based on ant colony algorithm for power edge computing. Secur Commun Netw 2021:1–9. https://doi.org/10.1155/2021/4868618
Soni A, Jain YK (2015) A bee colony based multi-objective load balancing technique for cloud computing environment
Piyush Gohel CK (2015) A novel honey bee inspired algorithm for dynamic load balancing in cloud environment. Int J Adv Res Electr Electron Instrum Eng 4(8):6995–7000. https://doi.org/10.15662/ijareeie.2015.0408025
Hybrid load balancing approach based on the integration of QoS and power consumption in cloud computing. Int J Adv Trends Comput Sci Eng 10(2):1079–1090. https://doi.org/10.30534/ijatcse/2021/841022021
Panda S, Gupta T, Handa SS (2017) A survey on honey bee foraging behavior and its improvised load balancing technique. SJ Impact Factor: 6 887 [Online]. Available: www.ijraset.com2039
Shahid MA, Islam N, Alam MM, Su’Ud MM, Musa S (2020) A comprehensive study of load balancing approaches in the cloud computing environment and a novel fault tolerance approach. IEEE Access 8:130500–130526. https://doi.org/10.1109/ACCESS.2020.3009184
Yang X-S, Deb S (2010) Cuckoo search via Levy flights, Mar 2010 [Online]. Available: http://arxiv.org/abs/1003.1594
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://doi.org/10.1109/MCI.2006.329691
Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5(3):6915. https://doi.org/10.4249/scholarpedia.6915
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, A., Vaidya, P., Patel, M., Doshi, N. (2023). An Analysis of Resource-Oriented Algorithms for Cloud Computing. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. ICTIS 2023. Lecture Notes in Networks and Systems, vol 719. Springer, Singapore. https://doi.org/10.1007/978-981-99-3758-5_46
Download citation
DOI: https://doi.org/10.1007/978-981-99-3758-5_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3757-8
Online ISBN: 978-981-99-3758-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)