Servers Placement Scheme Based on All-pay Auction Framework in Mobile Edge Computing
Yun Xia
TL;DR
The paper addresses maximizing service provider revenue in mobile edge computing by optimally placing edge servers for task offloading. It models a multiserver-multiuser MEC and derives valuation $v = A \sqrt{q/Q}$ with $F(v)$ uniform on [0, A] and $A = k \log_2 F_t$, and equilibrium bid $b_e = \int_0^v t \; dF^{n-1}(t)$; server revenue expressions $U_{server}^j = sum_{i=0}^{n_i} b_e^i - B - log_2 q_w$ and total revenue $sum_j U_{server}^j$ are used. Through simulations, it demonstrates that total revenue is maximized when the server-user ratio is approximately 25%, balancing supply and demand while avoiding idle capacity. This approach provides a practical mechanism for SPs to monetize MEC resources and informs scalable deployment strategies under competitive bidding.
Abstract
Task offloading plays a pivotal role in mobile edge computing, enabling terminal devices to enhance task execution efficiency and conserve energy. However, servers are reluctant to offer services without compensation. Currently, pricing mechanisms are commonly employed to incentivize servers to serve terminal devices, with servers earning revenue through payments from these devices. Given the rapid surge in terminal devices, determining the optimal number of servers placement for service providers (SPs) to maximize revenue is crucial. In this paper, we propose a server placement scheme based on an all-pay auction framework. Experimental simulations reveal that an optimal server-user ratio of approximately 25% maximizes SP profits.