Abstract

Hybrid cloud had evolved as the most popular choice for cloud solution, it enhances the flexibility of enterprises to process and manage data at a quicker pace by integrating both public and private clouds. However, achieving data reliability – consistency, availability and fault tolerance remains a major problem because of the dynamics and intricate nature of hybrid systems. Standard means of ensuring data reliability fail to adequately address these issues, especially in systems characterized by large amounts of data and multisystem running. This study aims at identifying how AI can be best integrated in a hybrid cloud computing system to make data more reliable.

This study presents a literature review of the current works focusing on HCSs, reliability issues concerning data, and intelligent approaches in clouds. The work examines the possible use of such key AI methodologies as ML, anomaly detection, predictive analysis, and fault diagnosis in the context of potential benefits for RL. A new AI architecture is presented to incorporate fault tolerance, predictive maintenance and consistency management into the HCS without the need for external middleware. It uses supervised and the unsupervised machine-learning models in simulated and real hybrid clouds to increase the fault tolerance; redundancy, and more importantly, failure predictions.

Based on the findings of the study, it can be clearly seen that applying the proposed work results in enhanced values of critical reliability parameters for example system availability, data integrity and time taken in fault recovery as opposed to the use of conventional reliability models. Furthermore, the proposed AI framework maintains versatility of integrating with essentially all types of hybrid cloud deployment models including an impressive scalability for complex enterprise applications across different industries. The discussion also covers more gamut area about the combined future of AI and hybrid cloud environment such as, it increases the operating efficiency, minimizes the down time and build customer satisfaction through proper data handling.

This study captures the need to account for the application of AI in analyzing the hybrid cloud computing models and offers practical recommendations to firms that want to enhance their cloud environments. Future research directions involve an investigation of higher-level AI methods, including reinforcement learning and federated learning and examining the potential use of innovative technologies like blockchain and quantum computing in enhancing the dependability and security of hybrid cloud systems.

Keywords

  • SIEM
  • security monitoring
  • alert prioritization
  • critical systems

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