# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 1 Introduction
### Paper Overview and Contributions  
This paper proposes an energy management solution applicable to 5G network slicing in which the service processing (i.e., the creation of network slices and corresponding services) is aware of the energy supply with the aim of optimizing power consumption, specifically by minimizing the power consumption from non-renewable energy sources. The proposal considers renewable energy sources, but non-renewable sources can also be used when extra energy is required. Efficient energy management involves minimizing consumption, achieving a specific reduction in consumption (e.g., 20% energy saving), or optimizing the available power consumption (utilization). In this work, we chose the last option, anticipating a future in which the penetration of green energy allows meeting all energy demand and requires adaptive mechanisms to leverages its generation. Moreover, we validated this approach in [19]. The considerations of the architectural framework and the enabling technologies needed for energy management, as well as the interaction between generation and consumption sides presented in [19], are taken as a baseline for the adaptive energy management solution presented in this paper. In this regard, this paper represents an evolution of our previous work in [19] towards an efficient and environmentally friendly energy-management for mobile networks. The proposal includes: (i) an architecture for adaptive energy management that considers provisioning from renewable and non-renewable sources, which is developed based on NFV and SDN technologies, and leverages the IoT massive connectivity provided by modern mobile networks; and (ii) various 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), all to optimize the available power consumption. In addition, this paper provides modeling of the stakeholders involved in energy management and consumption (i.e., the MNO and consumers), and the Integer Linear Programming (ILP) formulation corresponding to the problem of the adaptive energy management, which has been proven to be \(\mathcal{NP}\)-hard. To find the exact optimal solution, a brute-force search algorithmic strategy (OptTSNS) is proposed. Simulation results demonstrate that the proposed energy management solution allows for efficient energy consumption in the different slices and corresponding services in accordance with the available energy resources and customer requirements, thereby realizing feasible and efficient energy management for 5G networks. Due to the non-polynomial complexity of OptTSNS, this paper includes a discussion on the possible heuristic strategies for feasible future implementations of the energy management model and the proposed management strategies. The major contributions of this paper are summarized as follows:  
* An architecture proposal that, based on NFV, SDN, and IoT technologies, is able to adaptively manage the available energy, whether 100% renewable or not, in the context of network slicing
* Several management strategies for adaptive energy consumption at the service level, including intra-slice service prioritization, service rejection, time shifting for service execution, and service quality degradation
* The mathematical model of the stakeholders involved in energy management and consumption
* The ILP formulation corresponding to the adaptive energy management model
* The performance metrics needed to confirm the improvements in energy consumption achieved with the proposed approach
* An exact algorithmic strategy denoted as OptTSNS and its evaluation through extensive simulations to verify the effectiveness of the proposed solution as well as the improvements in terms of energy consumption and service processing
* Discussion of several heuristics solutions to tackle the complexity of the optimal solution  
Table 2 presents a list of the most relevant acronyms used throughout the paper.