Pricing Optimization in MEC Systems: Maximizing Resource Utilization Through Joint Server Configuration and Dynamic Operation

The resource allocation problem in Multi-access Edge Computing (MEC) has been widely studied to maximize its operation efficiency under limited resource constraint. However, the existing literatures overlooked the setup cost and the associated dynamic operations. In this work, we consider server configuration and overload in the multi-server scenario where servers are switched on/off depending on the network environment. A novel pricing mechanism maximizing the utility of base station (BS) monitoring multiple servers is proposed, which jointly optimizes the setup cost and server load. We aim to maximize the BS utility under one-day task requests, and divide the time into off-peak and peak periods based on task requests. In the off-peak period, we flexibly switch on/off servers for BS to reduce setup costs. In the peak period, to avoid overloading, we introduce crowdsourcing where servers as agents purchase idle resources from private users (PUs) for mobile users (MUs) and minimize MUs' cost by a contract-based knapsack algorithm. Lastly, a pricing mechanism is proposed to solve the BS utility maximization problem with an exploratory Upper Confidence Bound (UCB)-based algorithm adjusting server prices dynamically. Simulation results show that the proposed algorithm is superior to others in minimizing MUs cost and maximizing BS utility.