Gaussian Algorithms for Load Balancing and Secure Data Outsourcing in Cloud Networks

Abstract

This paper presents a State-of-the-Art Gaussian Distributive Optimized Congruential Cryptographic Deep Multilayer Perceptive Network (GD-DMPN) for achieving Load Balancing and Secure Data Outsourcing in Federated Cloud. The GD-DMPN can efficiently distribute and correlate data over many nodes, making it a valuable tool for data management in federated Clouds. The GD-DMPN can also exploit multiple layers of Perceptual Learning for enhanced data correlation and load balancing. As the world moves more and more towards digitalization, the demand for cloud services is increasing rapidly. Cloud services allow users to access their data and applications anywhere, anytime. However, the use of cloud services also raises security and privacy concerns. Several research studies have proposed using a federated cloud to address these concerns. Federated cloud is a type of cloud computing where a group of organizations cooperate to provide cloud services. Each organization in the federated cloud has its portion of the total resources available. This type of cloud computing has several advantages over other types, such as improved security and privacy and giving users more control over their data.

This study proposes a state-of-the-art Gaussian distributive optimized congruential cryptographic deep multilayer perceptive network for load balancing and secure data outsourcing in a federated cloud. Our proposed network is based on the Gaussian distribution, a well-known statistical distribution. We use the Gaussian distribution to distribute the resources among the organizations in the federated cloud. This ensures that each organization has access to the resources it needs while providing a degree of security and privacy. We also propose a deep multilayer perceptive network for our proposed system. This network is used to monitor the activities of the organizations in the federated cloud and to provide feedback to the system. This feedback is used to optimize the system and ensure the resources are used efficiently. Our proposed system can provide many benefits, such as improved security, privacy, and efficiency. In addition, our system can provide users with more control over their data. Our proposed system has the potential to revolutionize the federated cloud and provide users with a more secure and private way to access their data.