  Network slicing (NS) and multi-access edge computing (MEC) are new paradigms
which play key roles in 5G and beyond networks. NS allows network operators
(NOs) to divide the available network resources into multiple logical 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. NS can be constructed by instantiating a set of
virtual network functions (VNFs) on top of MEC cloud servers for provisioning
diverse latency-sensitive 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. To overcome time
complexity, 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.
