With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), for instance, faster response time and lower energy consumption, computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under the deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy using both a real-world case study on a MEC based UAV (Unmanned Aerial Vehicle) delivery system and extensive simulation experiments on the FogWorkflowSim platform for MEC based workflow applications. The evaluation results successfully demonstrate the effectiveness of our proposed strategy and its overall better performance than other representative strategies.

中文翻译：

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移动边缘计算中工作流应用的基于图的新型计算分载策略

随着移动边缘计算（MEC）的快速发展，对在边缘上运行复杂应用程序的需求日益增长。这些复杂的应用程序可以表示为明确指定任务相关性的工作流。为了获得更好的服务质量（QoS），例如更快的响应时间和更低的能耗，MEC环境中广泛使用了计算分流。但是，许多现有的计算卸载策略仅专注于独立的计算任务，却忽略了任务依赖性。同时，这些策略大多数基于搜索算法，例如粒子群优化（PSO），遗传算法（GA），这些算法通常很耗时，因此不适合MEC中许多对延迟敏感的复杂应用。所以，我们最近的工作中提出了一种高效的基于图的策略，但是它只能处理具有线性（即顺序）结构的简单工作流应用程序。为了解决这些问题，针对MEC中的工作流应用提出了一种新颖的基于图的策略。具体地说，此策略可以处理具有非线性（即并行，选择性和迭代）结构的复杂工作流应用程序。同时，通过基于图的划分技术，可以找到截止期限约束下终端设备能耗最低的卸载决策计划。我们已经基于基于MEC的无人机（UAV）交付系统的实际案例研究以及基于MEC的工作流应用程序在FogWorkflowSim平台上进行了广泛的模拟实验，从而全面评估了我们的策略。