Dynamic Load Balancing in Cloud-IoT Based on Neural Network Classification and GGA Optimization
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60629-8_7
Reference12 articles.
1. Benabbes, S., Hemam, S.M.: An approach based on genetic and grasshopper optimization algorithms for dynamic load balancing in CloudIoT. Comput. Inform. 42(2), 387–396 (2023)
2. Benabbes, S., Hemam, S.M.: An approach based on (Tasks-VMs) classification and MCDA for dynamic load balancing in the CloudIoT. In: Smart Energy Empowerment in Smart and Resilient Cities: Springer International Publishing, vol. 102, pp. 387–396 (2020). https://doi.org/10.1007/978-3-030-37207-1_41
3. Botta, A., Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and Internet of Things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)
4. Cunha, A., Borges, J., Loureiro, A.: Classification of botnet attacks in IoT using a convolutional neural network. In: Proceeding of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks, pp. 63–70 (2022)
5. Gulbaz, R., Siddiqui, A.B., Anjum, N., Alotaibi, A.A., Althobaiti, T., Ramzan, N.: Balancer genetic algorithm - a novel task scheduling optimization approach in cloud computing. Appl. Sci. 11(14), 6244 (2021)
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