Resilient and Latency-Aware Orchestration of Network Slices Using Multi-Connectivity in MEC-Enabled 5G Networks

Network slicing and multi-access edge computing (MEC) are new paradigms which play key roles in 5G and beyond networks. In particular, network slicing allows network operators (NOs) to divide the available network resources into multiple logical network slices (NSs) for providing dedicated virtual networks tailored to the specific service/business requirements. MEC enables NOs to provide diverse ultra-low latency services for supporting the needs of different industry verticals by moving computing facilities to the network edge. An NS can be constructed/deployed by instantiating a set of virtual network functions (VNFs) on top of MEC cloud servers for provisioning diverse latency-sensitive/time-critical communication services (e.g., autonomous driving and augmented reality) on demand at a lesser cost and time. However, VNFs, MEC cloud servers, and communication links are subject to failures due to software bugs, misconfiguration, overloading, hardware faults, cyber attacks, power outage, and natural/man-made disaster. Failure of a critical network component disrupts services abruptly and leads to users' dissatisfaction, which may result in revenue loss for the NOs. In this paper, we present a novel approach based on multi-connectivity in 5G networks to tackle this problem and our proposed approach is resilient against i) failure of VNFs, ii) failure of local servers within MEC, iii) failure of communication links, and iv) failure of an entire MEC cloud facility in regional level. To this end, we formulate the problem as a binary integer programming (BIP) model in order to optimally deploy NSs with the minimum cost, and prove it is NP-hard. Since the exact optimal solution for the NP-hard problem cannot be efficiently computed in polynomial time, we propose an efficient genetic algorithm based heuristic to obtain near-optimal solution in polynomial time. By extensive simulations, we show that our proposed approach not only reduces resource wastage, but also improves throughput while providing high resiliency against failures.