A novel secure fault tolerance Scheduling model in hybrid cloud using Deep learning technique

Abstract

The evolution of internet and modern computing facility attracts the small scale enterprises and entrepreneurs to host their services which are publicly viable, accessible and manageable by renting the resources at affordable price units. Moreover it promotes the development of ubiquitous applications towards general public in leading smarter life. The trending applications with cloud propose a set of challenges to be resolved in ensuring the proper cloud functionality. The system failure or security threats affect the smooth functioning of the cloud. To ensure the continuous operation of the cloud irrespective of failures, fault tolerance is incorporated through the concepts of redundancy and replication. The scope for fault tolerance still remains to be addressed effectively as with the modern technologies in order to avail uninterrupted services in a large scale. Security threats are constantly evolving and becoming more complex, and cloud computing is just as vulnerable as on-premise infrastructure. Due to this, it is essential to collaborate with a cloud provider who offers highest level of security that is suited to the environment. newlineScheduling is a pivotal issue when it comes to improve the performance of any cloud-based implementations. The heterogeneity and vibrant features of the resources in the cloud network causes the failure, which is inevitable. The failure in turn affects the reliability and availability of the cloud service. If a task is not completed, it can have an impact on other tasks. Hence, cloud computing requires a fault-tolerant, aware scheduling mechanism. Existing scheduling approaches focused on enhancing resource utilisation in the cloud without taking into account fault tolerance and security. To cope up with these issues newline