# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 7 Conclusions  
Network slicing is specified for 5G networks to provide new opportunities for service provisioning in order to increase efficiencies and improve revenue. Regarding energy consumption management, this feature is a key factor in the deployment and evolution of mobile networks, and it is also a crucial consideration. This paper analyzes the requirements, proposes an architecture, and presents a feasible and efficient solution for adaptive energy management in 5G network slicing that meets the requirements of energy efficiency and sustainability by current and future mobile communications networks. The proposal aims to optimize the consumption of available power, and specifically seeks for the minimization of power consumption of non-renewable energy sources. In this regard, our proposed energy management model prioritizes the use of renewable energy sources, but non-renewable energy sources can be used when extra energy is required. The architecture proposal covers the requirements for efficient, adaptive, and sustainable energy management; the mathematical model of the stakeholders involved in energy management and consumption in the 5G ecosystem; and several management strategies at service level such as an intra-slice prioritization scheme, service rejection, time shifting in service execution, and degradation in service quality (i.e., decrease in energy demand) with the aim of optimizing available power consumption. The proposal also presents the ILP formulation of the adaptive energy management model and its complexity analysis, which has been proven to be \(\mathcal{NP}\)-hard. To validate the operation of the proposed adaptive energy management model an exact optimal algorithmic strategy, OptTsNS, is presented, of exponential complexity that depends on the values of \(N\), \(u_{k,j}^{i}\), and \(Q\), as shown in Equation (28), and which reveals the need for heuristic approaches for scalable, faster, and less computationally demanding implementations. The evaluation of the exact solution for a particular case study allows us to verify the improvements obtained with the proposal in power consumption and utilization, in the increase of service processing, and in the minimization of the use of non-renewable energy sources. This latter feature is of paramount importance because it can potentially reduce the OPEX for the MNO and the energy footprint because of the operation of the mobile networks. Future work can include the following aspects: (i) incorporation of an inter-slices prioritization scheme to apply management strategies or control actions directly to entire network slices; (ii) extension of the energy management model to the end-user side to improve energy efficiency and management of battery units; (iii) implementation and evaluation of more scalable and faster algorithmic strategies, taking as a baseline the procedures, metrics, and results obtained for the optimal solution as well as the information of the possible heuristic strategies presented in Table 7; and (iv) application of the energy management strategies to other scenarios, e.g., for managing energy production and consumption in smart homes. Conceptualization, C.T. and X.H. ; methodology, C.T. and X.H. ; software, C.T. and X.H. ; validation, C.T. and X.H. ; formal analysis, C.T. and X.H. ; investigation, C.T. and X.H. ; resources, C.T. and X.H. ; data curation, C.T. and X.H. ; writing--original draft preparation, C.T. and X.H. ; writing--review and editing, C.T. and X.H. ; visualization, C.T. and X.H. ; supervision, C.T. and X.H. ; project administration, C.T. and X.H. ; and funding acquisition, X.H and C.T. All authors have read and agreed to the published version of the manuscript  
This work was supported by the Ministerio de Economia y Competitividad of the Spanish Government under project PID2019-108713 RB-C51.