AN INTELLIGENT NETWORK SLICING FRAMEWORK FOR DYNAMIC RESOURCE SHARING IN MULTI-ACCESS EDGE COMPUTING ENABLED NETWORKS

Abstract

Dynamic resource sharing in multi-access edge computing (MEC) enabled networks has gained tremendous interest in the recent past, paving the way for the realization of beyond fifth generation (B5G) communication networks. To enable efficient and dynamic resource sharing, Network Slicing has appeared as a promising solution, virtualizing the network resources in the form of multiple slices employed by the end-users requiring strict latency, proximate computations, and storage demands. In literature, network slicing is primarily studied in the context of communication resource slicing, and little research has been devoted to jointly slicing communication, energy, and MEC resources. In this paper, we, therefore, proposed a joint network-slicing framework that considers 1) communication resources, 2) compute resources, 3) storage resources, and 4) energy resources, and intelligently and dynamically shares the resources between different slices, aiming to improve tenants' overall utility. To this end, we formulated a utility maximization problem as Markov-chain Decision Process. We utilized a tenant's manager that employs a deep reinforcement learning technique named "deep deterministic policy gradient" to enable dynamic resource sharing. Simulation results reveal the effectiveness of the proposed scheme.