Through the analysis and research of reliability problems in the existing workflow scheduling algorithm, the paper proposed a reliability-based workflow strategy concerning the problems in improving the reliability of the entire workflow by sacrificing efficiency or money in some algorithms. Combining the reliability of tasks in workflow and duplication ideology, and taking full consideration of priorities among tasks, this strategy lessened failure rate in transmitting procedure and meantime shortened transmit time, so it not only enhanced overall reliability but also reduced makespan. Through the experiment and analysis, the reliability of cloud workflow in this strategy, tested by different numbers of tasks and different Communication to Computation Ratios (CCR), was proved to be better than the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and its improved algorithm named SHEFTEX, including the superiority of the proposed algorithm over the HEFT in the completion time.

In the existing genetic algorithms for permutation flow shop scheduling problem, the crossover and mutation operator is complex because of the processing sequence, the offspring is not similar to parent, and the algorithm easily falls into local optimum. To solve these problems, an improved genetic algorithm with priority-based value coding method and optimum limited operator was proposed. The coding method based on the priority values of the workpieces could avoid illegal coding, and the optimum limited operator could limit the propagation of the best individual to prevent falling into local optimum. The experiments show that this coding method is feasible and it can solve the practical problem when urgent workpieces must be processed firstly. The simulation results on benchmarks demonstrate that the proposed algorithm has superiority of smaller relative error and higher stable solution quality.

According to the research and analysis on the existing task scheduling strategy of scientific workflow under the cloud environment, a two-step task scheduling strategy was proposed. This strategy aimed at solving or alleviating the phenomenon of resource idle in Heterogeneous Earliest Finish Time (HEFT) algorithm and SHEFT algorithm. Along with the characteristics of cloud computing environment, it derives from the SHEFT algorithm. It can make the most use of the resources idle time and get the minimum makespan. The experiments and performance analysis for the scheduling strategy show that it has a significant improvement in the workflow makespan and resource utilization.