# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 2 Related Work
### Energy Efficiency and Energy Management in 5G Networks  
Improving energy efficiency has become a key pillar in the design of 5G networks due to economic and operational considerations, as well as environmental concerns [20]. In this regard, different solutions have been proposed to optimize or reduce energy consumption, which can be grouped under following broad categories: (i) resource allocation; (ii) network planning and deployment; (iii) activation of resources on-demand depending on traffic dynamic; (iv) hardware design; (v) improvements in the system operation (e.g., techniques to reduce interference); (vi) active user cooperation; and (vii) use of green energy complemented with energy-harvesting mechanisms (which can also be used to collect energy from radio signals over the air) [20]. Of these approaches, the use of green energy has gained momentum in recent years, and it represents a feasible and sustainable alternative to power mobile networks partially or even totally [21]. In this respect, different models have been proposed to characterize the production of renewable energy and the operation in the mobile networks. Even the possible interactions with smart grids to deliver demand-response schemas have been analyzed. Additionally, the need for management strategies in mobile networks to enhance integration and use of green energy has been evidenced [21], an issue that is solved with our proposal.  
Regarding energy management within the 5G ecosystem, the literature shows that the enabling technologies NFV and SDN can be used as a platform to deploy optimization models (mainly based on heuristic approaches) and management applications targeting cost-efficient resource and energy usage [22]. Based on energy consumption estimations or network parameters information (e.g., traffic load, radio coverage, equipment activation intervals, or active users), the NFV/SDN architectural framework can carry out actions such as optimized routing of traffic flows or allocation of physical (networking, computing, and storage) and/or virtual (e.g., virtual machines) resources with the aim of achieving energy savings and an overall reduction of consumption in the mobile network [23; 24]. In addition, NFV technology facilitates in 5G networks that the Virtual Network Functions (VNFs) can be dynamically scale-in/out to meet the desired performance level, a dynamic behavior, or to be adjusted to system capacity. These features can potentially reduce energy consumption, operating cost, and latency. In this regard, some adaptive and dynamic VNF scaling algorithmic strategies have been proposed in the literature [25; 26]. Although these procedures can reduce the energy footprint of 5G networks, they operate primarily on the network infrastructure and do not constitute an adaptive energy management system. Furthermore, these solutions do not consider the available energy supply for service processing or a multi-client approach, aspects which are considered in our proposal.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Acronym** & **Definition** & **Acronym** & **Definition** \\ \hline
3 GPP & 3 rd Generation Partnership Project & MNO & Mobile Network Operator \\
5G & 5 th Generation Mobile Network & NB-IoT & Narrow Band Internet of Things \\ eMBB & Enhanced Mobile Broadband & NFV & Network Functions Virtualization \\ ETSI & European Telecom. Standards Institute & NSI & Network Slice Instance \\ ICT & Infor. and Com. Technologies & OPEX & Operational Expenditures \\ ILP & Integer Linear Programming & SST & Slice/Service Type \\ IoT & Internet of Things & SDN & Software Defined Network \\ ITU & International Telecommunication Union & URLLC & Ultra Reliable Low Latency Com. \\ MANO & Management and Orchestration & V2X & Vehicle to X (e.g., Vehicle, Infrastructure) \\ mIoT & Massive IoT & VNF & Virtual Network Function \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of abbreviations and corresponding definitions.