With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

中文翻译：

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移动边缘计算中IoT批处理作业的收入最佳任务调度和资源管理

随着物联网（IoT）设备和技术的日益普及，微妙地提出了新兴的计算范例，即移动边缘计算（MEC），其设计目的是为了适应IoT场景的应用需求。在本文中，我们重点关注MEC环境中的动态任务调度和资源管理问题，其特定目标是实现边缘服务提供商获得的最佳收入。虽然大多数任务调度和资源管理算法是由整数编程（IP）问题制定的，并且以不受欢迎的NP-hard方式解决，但我们创新地研究了问题结构并确定了有利的属性，即完全单模约束。完全的单模性质进一步有助于设计一个等效的线性规划（LP）问题，该问题可以在多项式计算复杂度下有效且优雅地解决。为了评估我们提出的方法，我们基于现实的物联网数据集进行了仿真，以验证我们方法的有效性和效率。