Enhancing nano grid connectivity through the AI-based cloud computing platform and integrating recommender systems with deep learning architectures for link prediction

Abstract

Cloud computing has become ubiquitous in modern society, facilitating various applications ranging from essential services to online entertainment. To ensure that quality of service (QoS) standards are met, cloud frameworks must be capable of adapting to the changing demands of users, reflecting the societal trend of collaboration and dependence on automated processing systems. This research introduces an innovative approach for link prediction and user cloud recommendation, leveraging nano-grid applications and deep learning techniques within a cloud computing framework. Heuristic graph convolutional networks predict data transmission links in cloud networks. The trust-based hybrid decision matrix algorithm is then employed to schedule links based on user recommendations. The proposed model and several baselines are evaluated using real-world networks and synthetic data sets. The experimental analysis includes QoS, mean average precision, root mean square error, precision, normalized square error, and sensitivity metrics. The proposed technique achieves QoS of 73%, mean average precision of 59%, root mean square error of 73%, precision of 76%, normalized square error of 86%, and sensitivity of 93%. The findings suggest that integrating nano-grid and deep learning techniques can effectively enhance the QoS of cloud computing frameworks.