An Integrated Framework of RNN and MCDM SAW Method for Efficient Resource Provisioning in Cloud Computing

Cloud computing is vital for businesses and individuals, offering a flexible and scalable solution for data storage, processing, and access. As cloud usage expands, optimizing resource allocation and reducing costs becomes crucial. In this paper, we propose an RNN Hybrid resource allocation strategy for cloud computing. This strategy leverages recurrent neural networks to predict future resource demands of cloud applications and dynamically allocate resources accordingly. We integrate the MCDM (SAW) method and RNN algorithm to rank users and predict bidding prices. Evaluating our proposed strategy and experiments using a dataset generated by the Double auction algorithm, the results demonstrate superior resource utilization, application performance, and accurate bid value prediction compared to existing strategies. This RNN-based approach holds great potential for enhancing the efficiency and effectiveness of cloud computing systems.