Multi-access Edge Computing (MEC) has emerged as a flexible and cost-effective paradigm, enabling resource constrained mobile devices to offload, either partially or completely, computationally intensive tasks to a set of servers at the edge of the network. Given that the shared nature of the servers’ resources introduces high computation and communication uncertainty, in this paper we consider users’ risk-seeking or loss-aversion behavior in their final decisions regarding the portion of their computing tasks to be offloaded at each server in a multi-MEC server environment, while executing the rest locally. This is achieved by capitalizing on the power and principles of Prospect Theory and Tragedy of the Commons, treating each MEC server as a Common Pool of Resources available to all the users, while being rivarlous and subtractable, thus may potentially fail if over-exploited by the users. The goal of each user becomes to maximize its perceived satisfaction, as expressed through a properly formulated prospect-theoretic utility function, by offloading portion of its computing tasks to the different MEC servers. To address this problem and conclude to the optimal allocation strategy, a non-cooperative game among the users is formulated and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation, while useful insights about the users’ data offloading decisions under realistic conditions and behaviors are presented.

中文翻译：

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多服务器多访问边缘计算环境中的风险感知数据分载

多访问边缘计算（MEC）已经成为一种灵活且具有成本效益的范例，它使资源受限的移动设备能够将计算密集型任务部分或全部地分担给网络边缘的一组服务器。鉴于服务器资源的共享性质会带来很高的计算和通信不确定性，因此在本文中，我们在考虑最终用户决定在每台服务器上分担其计算任务的部分时，会考虑用户的风险寻求或损失规避行为。多MEC服务器环境，同时在本地执行其余操作。这是通过利用“前景理论”和“公地悲剧”的力量和原则，将每台MEC服务器视为可供所有用户使用的公共资源池，同时又轻而易举地实现的，因此，如果用户过度开发，则可能会失败。通过将其计算任务的一部分卸载到不同的MEC服务器，每个用户的目标将变得最大化其感知的满意度（通过适当制定的前景理论实用功能表示）。为了解决该问题并得出最佳分配策略，制定了用户之间的非合作博弈，并确定了相应的Pure Nash均衡（PNE），即最佳数据卸载，同时收敛了分布式低复杂度算法引入PNE。通过建模和仿真演示了所提出框架的性能和关键原理，同时还提供了有关在现实条件和行为下用户数据卸载决策的有用见解。