Slicing Allocation Optimization Based on MEC Service-Aware in 5G Smart Grids Enabled Surveillance System

Abstract

Smart Grids (SGs) expedite the secure, large-scale and efficient two-way communication between the power supply and management by virtue of sophisticated 5G communication infrastructure. In this paper, we propose an efficient strategy of 5G network slicing, with evolved mobile edge computing (MEC) and edge gateways (EGW), between aggregators (small base station, SBS) and SGs terminal units, for efficient service-aware (SA)-quality of service (QoS) synergy. There are notable concerns regarding certain slicing allocation efficiency issues to capture and fulfill the heterogeneous QoS requirements within 5G architectures. By dynamic slicing on the resource grid to determine the candidate slicing pool, the heterogeneous QoS for different services were satisfied via allocating and adjusting slicing resource in non-convex (NC) energy efficiency (EE) optimization mechanism. After pointing out that optimal problem is NC relaxation, we propose Charnes-Cooper transform and Lagrange Multiplier (CC-LM) based algorithm with a low complexity, for maximizing EE in SBS MEC system. Finally, numerical results indicate of convergence of the proposed mechanism and verify a higher degree of efficiency through resource assignment in such 5G MEC architecture.