CIF: Small: Collaborative Research: Communicating While Computing: Mobile Fog Computing Over Wireless Heterogeneous Networks

越来越多的应用，包括监视、医疗监测、自动翻译和游戏，依赖于移动的无线设备及时执行计算密集型任务的能力。这一要求与移动的设备应该依靠电池运行而不需要频繁充电的期望相冲突。移动的云计算是解决这一挑战的一个有前途的方案，即通过无线传输将计算密集型任务卸载给云服务提供商。然而，无线传输所带来的能量和延迟可能会抵消移动的云计算的潜在收益。该项目提出通过开发有效的、低复杂度的、可扩展的和灵活的卸载策略来解决所概述的问题，该策略在移动的雾计算架构上操作，其中小小区基站被赋予计算能力以提供邻近的无线接入和计算。从成功完成该项目中获得的见解将有利于其他需要大规模优化的令人兴奋的问题领域，包括大数据挖掘，信号处理，机器学习和智能电网。研究议程是由一个多学科的教育计划，目标是通过动手学习和实验活动的本科生和研究生的补充。还设想通过实习和合作机会进行工业合作。移动的雾计算网络中的计算和通信资源的层间优化产生非结构化非凸混合整数问题，其是未探索的和具有挑战性的，并且其公式化取决于移动的应用是否是可拆分的，即，可分割成可单独卸载或不可卸载的子任务。由于手头的问题不适合现有的迭代优化技术的应用，如差分凸规划，一类可扩展的和灵活的解决方案，可控的收敛性，复杂性和开销的基础上引入一个新的逐次凸逼近框架。在可拆分应用程序的情况下，分析和算法框架通过利用移动的应用程序的调用图表示的消息传递策略的应用程序来增强。

An increasing number of applications, including surveillance, medical monitoring, automatic translations and gaming, rely on the capability of mobile wireless devices to carry out computation-intensive tasks in a timely manner. This requirement conflicts with the expectation that mobile devices should run on a battery without needing frequent recharging. A promising solution to this challenge is mobile cloud computing, that is, the offloading of computation-intensive tasks to a cloud service provider by means of wireless transmission. However, the energy and latency entailed by wireless transmission may offset the potential gains of mobile cloud computing. This project proposes to tackle the outlined problem via the development of effective, low-complexity, scalable and flexible offloading strategies that operate over a mobile fog computing architecture, in which small-cell base stations are endowed with computing capabilities to offer proximate wireless access and computing. The insights gained from the successful completion of this project will be beneficial for a gamut of other exciting problem domains that require large-scale optimization, including big data mining, signal processing, machine learning, and smart grid. The research agenda is complemented by a multidisciplinary educational plan that targets both undergraduate and graduate students via hands-on learning and experimentation activities. Industrial collaboration is also envisaged through internship and co-op opportunities. The inter-layer optimization of the computation and communication resources in a mobile fog computing network yields unstructured nonconvex mixed-integer problems, which are unexplored and challenging, and whose formulation depends on whether the mobile applications are splittable, i.e., divisible into subtasks that can be individually offloaded, or not. Since the problems at hand do not lend themselves to the application of existing iterative optimization techniques, such as Difference-of-Convex programming, a class of scalable and flexible solution methods with controllable convergence, complexity and overhead is introduced based on a novel successive convex approximation framework. In the case of splittable applications, the analytical and algorithmic framework is augmented by the application of message passing strategies that leverage the call graph representation of the mobile applications.