Optimal Network Slicing for Service-Oriented Networks with Flexible Routing and Guaranteed E2E Latency

TL;DR

This paper introduces optimized network slicing strategies for 5G networks that ensure end-to-end latency, resource efficiency, and flexible routing through novel mathematical formulations, improving computational efficiency for large networks.

Contribution

It proposes two equivalent mixed binary linear programming formulations for network slicing that optimize energy efficiency while guaranteeing latency and resource constraints.

Findings

The second formulation is computationally more efficient for large networks.

Numerical results show the proposed methods outperform existing solutions.

The formulations effectively balance latency, resource use, and energy efficiency.

Abstract

Network function virtualization is a promising technology to simultaneously support multiple services with diverse characteristics and requirements in the 5G and beyond networks. In particular, each service consists of a predetermined sequence of functions, called service function chain (SFC), running on a cloud environment. To make different service slices work properly in harmony, it is crucial to appropriately select the cloud nodes to deploy the functions in the SFC and flexibly route the flow of the services such that these functions are processed in the order defined in the corresponding SFC, the end-to-end (E2E) latency constraints of all services are guaranteed, and all cloud and communication resource budget constraints are respected. In this paper, we first propose a new mixed binary linear program (MBLP) formulation of the above network slicing problem that optimizes the system energy efficiency while jointly considers the E2E latency requirement, resource budget, flow routing, and functional instantiation. Then, we develop another MBLP formulation and show that the two formulations are equivalent in the sense that they share the same optimal solution. However, since the numbers of variables and constraints in the second problem formulation are significantly smaller than those in the first one, solving the second problem formulation is more computationally efficient especially when the dimension of the corresponding network is large. Numerical results demonstrate the advantage of the proposed formulations compared with the existing ones.