# An Optimization-enhanced MANO for Energy-efficient 5G Networks

## TL;DR

This paper presents an optimization-based approach integrated into 5G network management to improve energy efficiency by jointly deciding node activation, function deployment, and traffic routing, tested on real and emulated networks.

## Contribution

It introduces a novel optimization framework for energy-efficient 5G network management that considers both forwarding and computational capabilities of nodes.

## Key findings

- Outperforms state-of-the-art methods in energy efficiency.
- Achieves near-optimal solutions in real-world scenarios.
- Demonstrates effectiveness on real and emulated networks.

## Abstract

5G network nodes, fronthaul and backhaul alike, will have both forwarding and computational capabilities. This makes energy-efficient network management more challenging, as decisions such as activating or deactivating a node impact on both the ability of the network to route traffic and the amount of processing it can perform. To this end, we formulate an optimization problem accounting for the main features of 5G nodes and the traffic they serve, allowing joint decisions about (i) the nodes to activate, (ii) the network functions they run, and (iii) the traffic routing. Our optimization module is integrated within the management and orchestration framework of 5G, thus enabling swift and high-quality decisions. We test our scheme with both a real-world testbed based on OpenStack and OpenDaylight, and a large-scale emulated network whose topology and traffic come from a real-world mobile operator, finding it to consistently outperform state-of-the art alternatives and closely match the optimum.