Joint Service Placement and Computation Offloading in Mobile Edge Computing: An Auction-based Approach

The emerging applications, e.g., virtual reality, online games, and Internet of Vehicles, have computation-intensive and latency-sensitive requirements. Mobile edge computing (MEC) is a powerful paradigm that significantly improves the quality of service (QoS) of these applications by offloading computation and deploying services at the network edge. Existing works on service placement in MEC usually ignore the impact of the different requirements of QoS among service providers (SPs), which is common in many applications such that online game requires extremely low latency and online video requires extremely large bandwidth. Considering the competitive relationship among SPs, we propose an auction-based resource allocation mechanism. We formulate the problem as a social welfare maximization problem to maximize effectiveness of allocated resources while maintaining economic robustness. According to our theoretical analysis, this problem is NP-hard, and thus it is practically impossible to derive the optimal solution. To tackle this, we design multiple rounds of iterative auctions mechanism (MRIAM), which divides resources into blocks and allocates them through multiple rounds of auctions. Finally, we conduct extensive experiments and demonstrate that our auction-based mechanism is effective in resource allocation and robust in economics.