Price-Aware Service Deployment in Hierarchical Mobile-Edge Computing

Mobile-edge computing (MEC) is considered as a promising solution to release pressure on the core network and reduce service response time. Edge nodes with storage and computation resources are able to cache various services and process tasks rather than offloading to remote clouds. However, it is difficult to make service deployment decisions appropriately since resources are limited in edge nodes and requirements of services are diverse. The hierarchical MEC structure in the 5G network causes extra complication, and the cost of service deployment aggravates the hardness, especially for service providers. In this article, we focus on the service deployment problem considering service caching, resource allocation, and task scheduling in the hierarchical MEC network, aiming at minimizing monetary cost. To address the heterogeneous limitations and requirements, we formulate the problem as a mixed-integer nonlinear programming problem and develop an iterative service deployment algorithm by exploiting Gibbs sampling. We make the service caching strategies of edge nodes iteratively. Furthermore, we transform the resource allocation and task scheduling optimization into the linear programming problem and employ a typical optimization function. Simulation results show that our algorithm always obtains minimal monetary cost for various number of services and task arrival rates, compared with benchmarks.