ComPA: Competition-Aware Dynamic Differential Pricing and Resource Allocation in Mobile Edge Computing via Gaming

Owing to remarkable advances in 5G and IoT, Mobile Edge Computing (MEC) has been extensively applied to meet the high latency requirements of computation-intensive applications. This paper explores the optimal resource management in MEC involving multiple competitive edge computing servers (ECSs), and presents a Competition-aware differential Pricing and resource Allocation method (ComPA). First, we propose a differential pricing mechanism that jointly analyzes the computing capability and resource usage rate to comprehensively quantify each MU’s use of ECS resources and give a differential per-second price, alleviating the resource underutilization in traditional schemes. Then, to co-optimize ECSs and MUs and ensure all ECSs have a fair chance to increase revenue, a differential pricing-based hierarchy game is developed. Specifically, ECSs and MUs play a Stackelberg game where ECSs price for higher profits and MUs subsequently offload at least-cost. Meanwhile, a non-cooperative game among competing ECSs is formulated. We design a price renewal algorithm that incorporates choice probabilities to find a suboptimal solution iteratively, offering ECSs the most competitive final pricing. MU’s optimal offloading decision is finally derived through convex optimization. Extensive experiments validate the notable superiority of ComPA over other advanced solutions in boosting ECS revenue and MU experience.