Improving Service Broker Policy of the Cloud Using Reinforcement Learning Through Equally Spread Current Execution Load Balancing Policy
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5504-2_27
Reference18 articles.
1. Chowdhury, S., Katangur, A.K., Sheta, A., Psayadala, N.R., Liu, S.: Genetic algorithm based service broker policy to find optimal datacenters in cloud services. Symmetry (2023)
2. Dubey, S., Singhal, V., Sahu, A.: Simulation of Scheduling & Load Balancing Algorithms for a Distributed Data Center by Using Service Broker Policy Over the Cloud, pp. 131–149. CRC Press eBooks (2023)
3. Shahid, M.A., Alam, M.M., Su’ud, M.B.M.: Performance evaluation of load-balancing algorithms with different service broker policies for cloud computing. Appl. Sci. 13(3), 1586 (2023)
4. Bhardwaj, M., Singh, K.D.: Study on efficient service broker policy for data center selection analyst for education system using cloud computing. In: Dahal, K., Giri, D., Neogy, S., Dutta, S., Kumar, S. (eds.) Internet of Things and Its Applications. LNEE, vol. 825, pp. 35–45. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7637-6_4
5. Panda, S.K., Ramesh, K., Indraneel, K., Ramu, M., Damayanthi, N.N.: Novel service broker and load balancing policies for CloudSim-based visual modeller. IEEE (2022)
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