Hybrid cloud service is a novel phenomenon that is strategized to estimate the failures in network and configure the Virtual Machines (VMs) in a cost-effective and timely manner. Amazon hybrid cloud offers Infrastructure as a Service (IaaS) in which the infrastructure such as servers, storage network, VMs etc can be dynamically accessed by global users either through manual methods or through automated web-based monitoring and management applications or else through console. Hybrid cloud computing model has been developed with an aim to handle big data since the existing algorithms find it challenging to compute the tasks. The novel hybrid cloud computing model is a result of need for an efficient computational asset that can handle huge scale sequencing experiments. In general, failure is an unavoidable phenomenon in large-scale computing systems. However, when hybrid cloud computing servers continuously fail in executing the tasks, it becomes complicated to handle fault tolerance, availability and reliability. This scenario demands the development of a novel approach that can compensate data loss and ensure automated recovery of the lost data. Further, it should also ensure the non-occurrence of such instances in future. The approach proposed, must enhance the availability of the system and performance of the overall network using VMs. This fault tolerance mechanism was tested under different scenarios involving data, application, selection and matching with faults, as per the requirements of the user. The failure detection model should have the capability to reconfigure the VMs in case of a failure. In this study that dealt with hybrid cloud infrastructure, host hypervisor was used to compare and contrast the failed clusters. The proposed prediction-recovery algorithm was able to identify superior components to achieve file delivery. Further, the AWS failure detection method used an estimation algorithm to achieve dynamic allocation of VMs. The occurrence of failure was hypothesized on the basis of failure dynamics. Further, failure prediction outcomes of the proposed model were also investigated.