This paper studies the joint optimization of edge node activation and resource pricing in edge computing, where an edge computing platform provides heterogeneous resources to accommodate multiple services with diverse pReferences. We cast this problem as a bi-level program, with the platform acting as the leader and the services as the followers. The platform aims to maximize net profit by optimizing edge resource prices and edge node activation, with the services’ optimization problems acting as constraints. Based on the platform’s decisions, each service aims to minimize its costs and enhance user experience through optimal service placement and resource procurement decisions. The presence of integer variables in both the upper and lower-level problems renders this problem particularly challenging. Traditional techniques for transforming bi-level problems into single-level formulations are inappropriate owing to the non-convex nature of the follower problems. Drawing inspiration from the column-and-constraint generation method in robust optimization, we develop an efficient decomposition-based iterative algorithm to compute an exact optimal solution to the formulated bi-level problem. Extensive numerical results are presented to demonstrate the efficacy of the proposed model and technique.

The emerging edge computing (EC) paradigm promises to provide low latency and ubiquitous computation to numerous mobile and Internet of Things (IoT) devices at the network edge. How to efficiently allocate geographically distributed heterogeneous edge resources to a variety of services is a challenging task. While this problem has been studied extensively in recent years, most of the previous work has largely ignored the preferences of the services when making edge resource allocation decisions. To this end, this article introduces a novel bilevel optimization model, which explicitly takes the service preferences into consideration, to study the interaction between an EC platform and multiple services. The platform manages a set of edge nodes (ENs) and acts as the leader while the services are the followers. Given the service placement and resource pricing decisions of the leader, each service decides how to optimally divide its workload to different ENs. The proposed framework not only maximizes the profit of the platform but also minimizes the cost of every service. When there is a single EN, we derive a simple analytic solution for the underlying problem. For the general case with multiple ENs and multiple services, we present a Karush–Kuhn–Tucker-based solution and a duality-based solution, combining with a series of linearizations, to solve the bilevel problem. Extensive numerical results are shown to illustrate the efficacy of the proposed model.