Abstract
Cloud is an important smart platform for digital applications to enrich communication services. However, massive data has made sharing resources and scheduling tasks a complex score. Also, any fault in one connected VM has degraded the entire cloud system. Considering these issues, the load balancing objective was taken into consideration. Moreover, the current study has implemented a novel Fruitfly-based transfer learning for sharing the required resources to each Virtual Machine (VM) for the task execution. Initially, the Virtual Machines (VMs) were created with different user tasks then the load was balanced by equally sharing the burden with other connected VMs. Consequently, the task ordering function was performed based on priority, and the required resources were assigned. Moreover, the planned model was tested in the python platform, and the metrics were measured. Also, the improved score for enhancing cloud services is validated by performing a comparative analysis. The shortest duration for the job scheduling process is 180 s, execution time 700 s and response time 15 s. These outcomes are better than the compared conventional models. Hence, this model efficiently balances the data load in the cloud computing environment, improving the cloud services.
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Edward Gerald, B., Geetha, P. & Ramaraj, E. A fruitfly-based optimal resource sharing and load balancing for the better cloud services. Soft Comput 27, 6507–6520 (2023). https://doi.org/10.1007/s00500-023-07873-y
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DOI: https://doi.org/10.1007/s00500-023-07873-y