Workload execution is composed by mapping larger tasks onto heterogeneous environments such as cloud platforms for enhancing the efficiency of workload resource management techniques. Execution of scientific workflow on a cloud platform is time-consuming, expensive, and requires a fault-tolerance guarantee. The existing methodology has emphasized on minimizing processing time to reduce costs. However, the processing cost can be reduced by minimizing energy consumption. In providing fault-tolerance while meeting the workload quality of service requirement the task is offloaded to a new physical machine; Hence, this increases energy consumption and thereby increases the cost of workload execution. In addressing the research challenges this paper presents the fault-tolerant aware (FTA) workload resource management (WRM) technique.First, the FTA-WRM optimizes processing and communication costs as an energy constraint leveraging the dynamic voltage frequency scaling technique.Then, a task offloading mechanism is modeled as an energy constraint with an application delay requirement for providing fault tolerance into the FTA-WRM. The experiment outcome shows the FTA-WRM significantly improves processing and energy efficiency in comparison with the existing workload resource management technique.

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