Survivable Probability of Network Slicing with Random Physical Link Failure

TL;DR

This paper introduces a new metric called survivable probability to evaluate the reliability of 5G network slices under random physical link failures, and proposes methods to identify optimal protecting spanning tree sets to enhance reliability.

Contribution

It defines survivable probability for network slices, proves the existence of base protecting spanning tree sets with equivalent survivability, and develops formulations to generate reliable 5G network slices.

Findings

Survivable probability effectively quantifies slice reliability.

Existence of base protecting spanning tree sets with equal survivability.

Mathematical formulations enable reliable network slice generation.

Abstract

The fifth generation of communication technology (5G) revolutionizes mobile networks and the associated ecosystems through the integration of cross-domain networks. Network slicing is an enabling technology for 5G as it provides dynamic, on-demand, and reliable logical network slices (i.e., network services) over a common physical network/infrastructure. Since a network slice is subject to failures originated from disruptions, namely node or link failures, in the physical infrastructure, our utmost interest is to evaluate the reliability of a network slice before assigning it to customers. In this paper, we propose an evaluation metric, \textit{survivable probability}, to quantify the reliability of a network slice under random physical link failure(s). We prove the existence of a \textit{base protecting spanning tree set} which has the same survivable probability as that of a network slice. We propose the necessary and sufficient conditions to identify a base protecting spanning tree set and develop corresponding mathematical formulations, which can be used to generate reliable network slices in the 5G environment. In addition to proving the viability of our approaches with simulation results, we also discuss how our problems and approaches are related to the Steiner tree problems and present their computational complexity and approximability.