AI-Powered Federated Task Scheduling and Self-Healing Framework in Dynamic Cloud Systems

Abstract

Federated cloud environments have emerged to integrate multiple cloud providers like AWS, Azure, and Google Cloud seamlessly into cloud computing. Optimising resource utilisation and ensuring high availability in such environments pose significant challenges. This paper comprehensively investigates federated task scheduling algorithms and self-healing mechanisms in autonomous federated cloud setups. The research objectives include the development of an independent task-scheduling algorithm capable of intelligently distributing computing tasks across federated clouds based on workload characteristics, resource availability, and network latency. Furthermore, the study investigates implementing self-healing mechanisms to detect faults and performance degradation, triggering automatic recovery processes for uninterrupted service availability. The proposed approaches are evaluated through real-world experiments, considering diverse cloud workloads and failure scenarios, focusing on resource utilisation efficiency, system performance, and the effectiveness of the self-healing mechanisms in mitigating cloud failures and maintaining seamless operations within the federated environment.