The edge computing model The cloud has grown dramatically through using edge computing, which greatly aids IoT and mobile devices in completing complex tasks. However, fast progress leads to the disregard of security vulnerabilities in edge computing systems and associated enabled applications, that has been one of the most significant limits in smart cities. Loud infrastructures are often made up of heterogeneous servers that host several virtual machines with possibly varying specs and variable resource use. This causes several challenges with resource allocation, such as energy saving, fault tolerance, task balance, and so on. Fault tolerance within cloud computing is a major difficulty these days. The fundamental challenges with fault tolerance in cloud computing are detection as well as recovery. To address these issues, numerous fault tolerance strategies have been developed to decrease defects. As a result, the goal of this article is to develop and test an inception-based LSTM model for predicting defect and job termination status (i.e. failure or success). The proposed approach has an accuracy of 98% and a proportion of true positives of 99%. The false positive rate, on the other hand, is 12%, which is much greater than the results at the task level.

Fault tolerance, Security, cloud computing, Fault Tolerance, Failure Prediction