Server configuration optimization in mobile edge computing: A cost-performance tradeoff perspective

Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint; (2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.

Automated summary

This study investigates the optimization of server configuration in an MEC environment by treating each edge server as an M/M/m queueing model and establishing performance and cost models. Two optimization problems based on cost and performance constraints are formulated and solved using fast numerical algorithms to help service providers achieve different goals. Extensive numerical simulation examples demonstrate the effectiveness of the proposed algorithms in controlling the tradeoff between investment cost and service quality for MEC service providers.