移动用户设备（UEs）和应用已经受到了广泛的欢迎。但是在UE上运行计算密集型应用会受到计算资源和电池容量的限制。移动边缘计算（MEC）能够将计算和存储资源下沉到网络边缘，通过计算卸载的方式使得资源有限的UE能够处理计算密集型和延时关键型应用。虽然MEC能够有效减少UE的能耗和应用的延时，但是数据安全、网络失连和虚拟机故障等重要问题在该领域尚未深入研究。基于以上分析，本项目主要研究MEC环境下面向安全感知及容错机制的工作流调度技术。提出的安全感知工作流调度机制，通过使用粒子群优化算法在满足截止时间和风险率的限制条件下最小化UE的能耗；针对D2D通信连接失败的问题，提出基于服务迁移的网络容错机制来最小化工作流的执行时间；提出的虚拟机容错机制，通过子完工时间分配及调整、复制和检查点技术，在满足截止时间条件下最优化计算资源使用。根据实际应用场景，搭建实验平台对所提方法进行测试、验证和改进。

Mobile user equipment (UEs) and applications have gained extensive popularity. However, running computation-intensive applications at the UEs is constrained by its limited computation resources and battery capacity. The emerging of mobile edge computing (MEC) can push computation and storage resources to the network edges so as to enable computation-intensive and latency-critical applications at the resource-limited UEs by computation offloading. Although MEC can reduce the energy consumption of UE and application latency effectively, there are important problems, notably those of data security, network failure, virtual machine (VM) fault, which have not been well studied in this domain. Based on the above descriptions, this proposal focuses on security-aware and fault-tolerant workflow scheduling in mobile edge computing environment. We propose a security-aware workflow scheduling mechanism, based on particle swarm optimization (PSO), to minimize the energy consumption of UE while meeting the deadline and risk rate constraints. In order to solve the link disconnection problem of D2D communication, we introduce a fault-tolerant scheduling mechanism for network failure, which is based on the service migration, to minimize the execution time of workflow application. We develop a fault-tolerant scheduling mechanism for virtual machine (VM) failure, which uses the sub-makespan allocation and adjustment, replication and checkpointing techniques to optimize the MEC computation resource under the deadline constraint. According to the actual application scenario, we also establish an experimental platform to test, validate and improve our proposed approaches.