Energy monitoring and management in 5G integrated fronthaul and backhaul

Abstract

Energy efficiency is likely to be the litmus test for the sustainability of upcoming 5G networks. Before the new generation of cellular networks are ready to roll out, their architecture designers are motivated to leverage the SDN technology for the sake of its offered flexibility, scalability, and programmability to achieve the 5G KPI of 10 times lower energy consumption. In this paper, we present Proofs-of-Concept of Energy Management and Monitoring Applications (EMMAs) in the context of three challenging, realistic case studies, along with a SDN/NFV-based MANO architecture to manage converged fronthaul/backhaul 5G transport networks.

Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies

“…To choose the best combination of services (i.e., the optimal distribution of services in time, with a P D (P Dcomb ) that produces minimization of P NR and, consequently, the optimal use of P R ), the cost functions defined in Section 4.2.3 are used. Specifically, the optimal solution delivered by OPTTSNS corresponds to the combination of services that produces the minimum cost function, as shown in Equation (22). To quantitatively verify the improvements obtained with OPTTSNS, the performance metrics defined in Section 5.1 are used and a numerical analysis is performed on a particular case study in Section 5.3.…”

“…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, the optimal scheduling of N services (i.e., the optimal combination of services) that leads to optimal energy consumption is given by the minimum value of cost function (minimum value in Equation 17). Thus, the objective of the proposed energy management solution is to minimize the total cost function, as shown in Equation (22). For the implementation of the adaptive energy management model represented by its cost function in Equation 22there are a number of different possible algorithmic solutions (optimal and sub-optimal).…”

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

“…The impact of software defined networking (SDN) on energy-efficiency was explored in [56]. The tremendous increase in the user density in a given area not only demands an energy efficient hardware but also demands for certain modifications in the control plane.…”

“…It comprises upon a context information module for collection of data for mobility, a statistics module for storing the contextual data and updating it regularly, and lastly the management module for consuming this data and making real time moves in the network by switching on the nodes as the train approaches and switching them off when it leaves. Significant energy savings ranging between 10 to 60% were demonstrated using the real life data by switching on the nodes exactly when needed and keeping them asleep otherwise [56].…”