## 4 Algorithm Description
### The Deadline Preference Dispatch Scheduling (DPDS) Algorithm
The experiment generated five task sets, and each task set has an average of 100 tasks. The average size of each task is four units. The deadline for each task is set to a random value within 200-400 s after the task arrives. Other parameters settings are the same as the Section 5.1. In the experiment, we assume a task request was submitted to the server every two minutes and the task completion status is tested every 100 min. Figure 11 shows the number of the completed tasks and the task completion rate of the three algorithms. Figure 11 shows the experimental results. It can be seen that the IDCS algorithm has obvious advantages on dynamic task sets, regardless of the number of the completed tasks or the task completion rate. Through the above experimental results, the validity of the IDCS algorithm proposed in this paper is further proved. ## 6 Conclusions

In this paper, we propose two novel dynamic task scheduling algorithms to solve the task scheduling problem with deadline constraint in VC platforms. One is the DPDS algorithm, and the other is the IDCS algorithm. In addition, by analyzing the characteristics of tasks and volunteer nodes in VC platforms, this paper uses a new risk prediction model in IDCS algorithm, which can predict the completion risk of each task. A lot of experiments based on a real-world dataset demonstrate that our proposed algorithms can solve the dynamic task scheduling problem with deadline constraint. And compared to the existing algorithms, the IDCS algorithm can maximize the number of task completions within the deadline. The task scheduling problem in VC platforms is very important, and further study should be done to improve the scheduling algorithm. In future, we will consider more factors that may affect task scheduling in VC platforms. [PERSON] designed and wrote the paper; [PERSON] supervised the work; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data. All authors have read and approved the final manuscript. **Acknowledgments:** This work was supported by the National Social Science Foundation of China (No. 15 BYY028) and Dalian University of Foreign Languages Research Foundation (No. 2015 XJQN05). **Conflicts of Interest:** The authors declare no conflict of interest. Figure 11: Performance comparisons of the three algorithms on the dynamic task set: (**a**) Comparison of task completion rate; (**b**) comparison of the number of tasks completed. ## References

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