Federated Learning-Driven IoT Request Scheduling for Fault Tolerance in Cloud Data Centers

Cloud computing is a virtualized and distributed computing model that provides resources and services based on demand and self-service. Resource failure is one of the major challenges in cloud computing, and there is a need for fault tolerance mechanisms. This paper addresses the issue by proposing a multi-objective radial kernelized federated learning-based fault-tolerant scheduling (MRKFL-FTS) technique for allocating multiple IoT requests or user tasks to virtual machines in cloud IoT-based environments. The MRKFL-FTS technique includes Cloud RAN (C-RAN) and Virtual RAN (V-RAN). The proposed MRKFL-FTS technique comprises four entities, namely, IoT devices, cloud servers, task assigners, and virtual machines. Each IoT device generates several service requests and sends them to the control server. At first, radial kernelized support vector regression is applied in the local training model to identify resource-efficient virtual machines. After that, locally trained models are combined, and the resulting model is fed into the global aggregation model. Finally, using a weighted round-robin method, the task assigner allocates incoming IoT service requests to virtual machines. This approach improves resource awareness and fault tolerance in scheduling. The quantitatively analyzed results show that the MRKFL-FTS technique achieved an 8% improvement in task scheduling efficiency and fault prediction accuracy, a 36% improvement in throughput, and a 14% reduction in makespan and time complexity. In addition, the MRKFL-FTS technique resulted in a 13% reduction in response time. The energy consumption of the MRKFL-FTS technique is reduced by 17% and increases the scalability by 8% compared to conventional scheduling techniques.