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Edge Computing (EC) allows users to access computing resources at the networkfrontier, which paves the way for deploying delay-sensitive applications suchas Mobile Augmented Reality (MAR). Under the EC paradigm, MAR users connect tothe EC server, open sessions and send continuously frames to be processed. TheEC server sends back virtual information to enhance the human perception of theworld by merging it with the real environment. Resource allocation arises as acritical challenge when several MAR Service Providers (SPs) compete for limitedresources at the edge of the network. In this paper, we consider EC in amulti-tenant environment where the resource owner, i.e., the Network Operator(NO), virtualizes the resources and lets SPs run their services using theallocated slice of resources. Indeed, for MAR applications, we focus on twospecific resources: CPU and RAM, deployed in some edge node, e.g., a centraloffice. We study the decision of the NO about how to partition these resourcesamong several SPs. We model the arrival and service dynamics of users belongingto different SPs using Erlang queuing model and show that under perfectinformation, the interaction between the NO and SPs can be formulated as asub-modular maximization problem under multiple Knapsack constraints. To solvethe problem, we use an approximation algorithm, guaranteeing a bounded gap withrespect to the optimal theoretical solution. Our numerical results show thatthe proposed algorithm outperforms baseline proportional allocation in terms ofthe number of sessions accommodated at the edge for each SP.

Multiple Resource Allocation in Multi-Tenant Edge Computing via Sub-modular Optimization