# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 1 Introduction
The proposal considers the manageability of the ECs, through massive connectivity and IoT technologies, and the dynamic and programmable behavior of management strategies that are deployed in an NFV domain and operating on scalable cloud computing infrastructures. This paper starts with an overview of the components of an NFV-enabled DR ecosystem. Subsequently, it presents the operating requirements and management mechanisms for adaptive energy management conditioned to the availability and with application to small and large-scale IoT scenarios. The proposal also includes the mathematical characterization of the ES and the ECs, as well as the adaptive consumption model using an Integer Linear Programming (ILP) formulation. The ILP model allows for us to identify the complexity of the problem and the possible algorithmic strategies to carry out adaptive energy management. In this context, this paper presents an algorithmic strategy, defined as OptTs, which is based on a brute-force search mechanism and solves the ILP optimally. Given that OptTs present a non-linear complexity, some heuristic strategies are analyzed in the scope of the problem. Subsequently, a fast heuristic strategy identified as FastTs that is based on a pre-partitioning method is implemented. The optimal exact and heuristic strategies are evaluated through extensive simulations. The results are compared with a baseline case, in which no management solution is applied. The simulation results in different scenarios validate the performance of the proposed adaptive energy management solution and demonstrate that the algorithmic strategies OptTs and FastTs, when deployed at the NFV domain, produces improvements in power consumption and demand processing. The major contributions of this paper are summarized, as follows:  
* The description of an NFV-enabled energy management ecosystem, the requirements, and the mechanisms to perform adaptive energy management of available supply, whether or not it is renewable. * The ILP formulation of the adaptive energy management model. * An exact algorithmic strategy, defined as OptTs, which based on a brute-force search combinatorial analysis solves the ILP optimally. * The discussion of possible algorithms to tackle the hardness of the ILP and the computational complexity of OptTs. * A heuristic solution defined as FastTs that based on a prepartitioning method produces solutions with a reduced running time (as compared to the optimal) and is applicable to large-scale environments. * The evaluation of the strategies OptTs and FastTs through extensive simulations in order to confirm the improvements in energy consumption and demand processing. Tables 1 and 2, respectively, present a list of the acronyms and most relevant notations used throughout the paper. The rest of the paper is organized, as follows. Section 2 discusses the related work. Section 3 introduces the problem of efficient energy consumption, the requirements, and the management mechanisms for the adaptive consumption of available energy. Section 4 presents the ILP formulation related to the energy management model. Section 5 describes OptTs for solving the ILP problem optimally. A discussion of possible heuristic algorithms is addressed in Section 6. The heuristic solution, FastTs, is presented in Section 7. Section 8 presents an overview of the deployment of the algorithmic strategies in the NFV domain. The evaluation results are discussed in Section 9. Finally, the conclusions and future work are drawn in Section 10.