Defending the Overloading System in Cloud Computing using Preamble Fuzzy Estimator based RNN
DOI:
https://doi.org/10.52783/jns.v14.1900Keywords:
Fuzzy Logic, Load Balancing, RNN, Resource Allocation, Server overload, Server OptimizationAbstract
Cloud server overloading poses a significant challenge in modern computing environments where scalability and reliability are paramount. Cloud server overloading occurs when the demand for resources exceeds the server's capacity, resulting in performance degradation or even system failures. Preventive measures such as Load balancing, Auto-scaling, Resource Monitoring, Predictive Analytics, Content delivery networks, Caching mechanisms, and Distributed Databases are used to manage cloud server overloading. This work is indented to introduce a novel preventive methodology using Fuzzy logic and Recurrent Neural Network (RNN) to diminish server overloading in cloud environments. Preamble Fuzzy Load Estimator and RNN based Dynamic Resource Allocator are the two models integrated in this work – titledas “Defending the Overloading System in Cloud Computing using Preamble Fuzzy Estimator based RNN” (PFEROD). The proposed work is developed and tested in a real-time cloud environment to measure the benchmark parameters such as Resource utilization, Balance Degree, Average Response Time, Migration Cost, Throughput based on number tasks and Throughput based on number of Virtual Machines. A notable about of stability in the performance score is achieved by PFEROD putting in effort.
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