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The emerging edge computing paradigm promises to provide low latency andubiquitous computation to numerous mobile and Internet of Things (IoT) devicesat the network edge. How to efficiently allocate geographically distributedheterogeneous edge resources to a variety of services is a challenging task.While this problem has been studied extensively in recent years, most of theprevious work has largely ignored the preferences of the services when makingedge resource allocation decisions. To this end, this paper introduces a novelbilevel optimization model, which explicitly takes the service preferences intoconsideration, to study the interaction between an EC platform and multipleservices. The platform manages a set of edge nodes (ENs) and acts as the leaderwhile the services are the followers. Given the service placement and resourcepricing decisions of the leader, each service decides how to optimally divideits workload to different ENs. The proposed framework not only maximizes theprofit of the platform but also minimizes the cost of every service. When thereis a single EN, we derive a simple analytic solution for the underlyingproblem. For the general case with multiple ENs and multiple services, wepresent 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 theproposed model.

A Bilevel Programming Framework for Joint Edge Resource Management and Pricing