Abstract
As cloud computing is growing increasingly and clients are demanding more resources and better performance, load balancing for the cloud has become a very interesting and relevant research field. Several algorithms have been suggested to provide successful frameworks and algorithms to allocate the requests of the client to available cloud nodes. These techniques are aimed at enhancing the overall efficiency of the cloud and delivering more enjoyable and effective services for the customer. One of the most significant research challenges in cloud computing is the use of energy-aware technologies along with the management of service level agreements. In this article, we discuss the numerous algorithms suggested in cloud computing to solve the problem of energy-effective techniques.
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
Stavrinides, G.L., Karatza, H.D.: An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Gener. Comput. Syst. 96, 216–226 (2019)
Devaraj, A.F.S., Elhoseny, M., Dhanasekaran, S., Lydia, E.L., Shankar, K.: Hybridization of firefly and Improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. J. Parallel Distrib. Comput. (2020)
Arulkumar, V., Bhalaji, N.: Performance analysis of nature inspired load balancing algorithm in cloud environment. J. Ambient Intell. Humanized Comput. 1–8 (2020)
Bhattacherjee, S., Das, R., Khatua, S., Roy, S.: Energy-efficient migration techniques for cloud environment: a step toward green computing. J. Supercomput. 1–29 (2019)
He, K., Li, Z., Deng, D., Chen, Y.: Energy-efficient framework for virtual machine consolidation in cloud data centers. China Commun. 14(10), 192–201 (2017)
Bharathi, P.D., Prakash, P., Kiran, M.V.K.: Energy efficient strategy for task allocation and VM placement in cloud environment. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1–6. IEEE (2017)
Mapetu, J.P.B., Kong, L., Chen, Z.: A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing. J. Supercomput. 1–42 (2020)
Ranjbari, M., Torkestani, J.A.: A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers. J. Parallel Distrib. Comput. 113, 55–62 (2018)
Nazir, B.: QoS-aware VM placement and migration for hybrid cloud infrastructure. J. Supercomput. 74(9), 4623–4646 (2018)
Khoshkholghi, M.A., Derahman, M.N., Abdullah, A., Subramaniam, S., Othman, M.: Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access 5, 10709–10722 (2017)
Tarafdar, A., Debnath, M., Khatua, S., Das, R.K.: Energy and quality of service-aware virtual machine consolidation in a cloud data center. J. Supercomput. 1–32 (2020)
Haghshenas, K., Mohammadi, S.: Prediction-based underutilized and destination host selection approaches for energy-efficient dynamic VM consolidation in data centers. J. Supercomput. 1–18 (2020)
Yaghoubi, M., Maroosi, A.: Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments. Simul. Modell. Practice Theory 102090 (2020)
Mandal, R., Mondal, M.K., Banerjee, S., Biswas, U.: An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing. J. Supercomput. 1–20 (2020)
Li, Z., Yu, X., Yu, L., Guo, S., Chang, V.: Energy-efficient and quality-aware VM consolidation method. Future Gener. Comput. Syst. 102, 789–809 (2020)
Gupta, A., Bhadauria, H.S., Singh, A.: SLA-aware load balancing using risk management framework in cloud. J. Ambient Intell. Humanized Comput. 1–10 (2020)
Paneru, D.R., Madhu, B.R., Naik, S.: A survey for energy efficiency in cloud data centers
Ali, S.A., Affan, M., Alam, M.: A study of efficient energy management techniques for cloud computing environment. arXiv preprint arXiv:1810.07458 (2018)
Zhou, Z., Yu, J., Li, F., Yang, F.: Virtual machine migration algorithm for energy efficiency optimization in cloud computing. Concurrency Comput. Practice Experience 30(24), (2018)
Haghighi, M.A., Maeen, M., Haghparast, M.: An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Wireless Pers. Commun. 104(4), 1367–1391 (2019)
Kumar, G.G., Vivekanandan, P.: Energy efficient scheduling for cloud data centers using heuristic based migration. Cluster Comput. 22(6), 14073–14080 (2019)
Saadi, Y., El Kafhali, S.: Energy-efficient strategy for virtual machine consolidation in cloud environment. Soft Comput. 1–15 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, C., Tiwari, P.K., Agarwal, G. (2022). Energy-Efficient Resource Allocation Approaches for Cloud Computing Systems: A Survey and Taxonomy. In: Somani, A.K., Mundra, A., Doss, R., Bhattacharya, S. (eds) Smart Systems: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2877-1_44
Download citation
DOI: https://doi.org/10.1007/978-981-16-2877-1_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2876-4
Online ISBN: 978-981-16-2877-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)