Mobile edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish mobile users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose learning-based pricing mechanisms where no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency.
As a main driver of 5G, the future mobile network is going to create a hyper‐connected Internet of Everything (IoE) world which requires the network to be scalable, versatile, and energy smart. The convergence of communication and computing is a promising solution of IoE while the introduction of fog/edge computing makes this possible in the future mobile networks. This article provides a comprehensive survey of the fog/edge‐computing‐enabled mobile network. Specifically, the motivation of introducing fog/edge computing, the standardization of fog/edge computing in 5G, and the mobile communication network architecture with fog/edge computing are reviewed. The challenges such as the scalability and extreme heterogeneity of resource management in IoE, and the advantages of using fog/edge‐based architecture, are also discussed. Under the fog/edge‐computing‐driven mobile communication network architecture, a distributed resource allocation mechanism based on economics is presented. The economics models and analysis study the underlying, and possibly conflicting objectives and incentives of various stakeholders, with pricing solutions providing a way for the optimization of objectives like social welfare, profit, or fairness. As such, it can be a potential powerful tool in solving the aforementioned challenges. While pricing for resource allocation has a vast existing literature, the analysis and schemes cannot be implemented directly due to the unique characteristics of 5G cloud‐fog/edge networks, hence providing a vast scope for potential research. In light of this, we finally outline some unique characteristics to consider when applying economics to aid resource allocation in 5G cloud‐fog/edge networks.
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