Hybrid fault tolerant cost aware mechanism for scientific workflow in cloud computing

Cloud computing provides solutions for diverse commercial and academic applications which is the primary goal. Scientific workflows are used in the cloud-computing environment to analyses large-scale scientific applications. For scientific workflows, many data is required, and a single scientific workflow that includes hundreds of stages, depending on the application's time restrictions, task failures, money limits, incorrect task organization, and task management issues can all hinder the implementation of scientific methods. In light of this, a cloud-based scientific workflow management and scheduling system that is fault-tolerant and data-oriented method are proposed. This research designs a novel hybrid cost-aware fault tolerant (HCFT) mechanism for minimizing the cost. Moreover, HCFT integrates optimal clustering and efficient resource utilization through parallel and distributed execution. Novelty of HCFT lies in novel clustering of the similar task for improvisation, CyberShake, laser interferometer gravitational wave observatory (LIGO), Montage, and sRNA identification protocol using high throughput technology (SIPHT) processes are used in the simulations to evaluate the performance of the proposed approach.