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

Distributed, Parallel, and Cluster Computing