Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Green <s> Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Green <s> It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective. <s> BIB002
Large Scale MEC Systems. MEC servers are expected to be deployed close to end users, along with small cells, and this will result in dense deployments, which still raise the concern of energy consumption in wireless systems. Since MEC servers are dimensioned for high speed computation, they still consume energy even during their idle state, similar to the always on approach of eNBs, and this calls for the development of means that will allow the servers to consume energy in a way that is proportional to their computational load BIB001 . Another option to save energy in idle MEC servers is to adopt sleep modes (which have also been proposed as an energy saving strategy for BSs). However, such approach might degrade the user's QoE; therefore proper workload variation prediction strategies have to be considered. In this way, it can be determined which MEC servers are to be deactivated and which ones are to remain active to handle mobility with minimal impact for the users. Moreover, by exploiting the spatial diversity in the workload patterns, MEC servers can coordinate to serve mobile users according to their location BIB002 . This load balancing approach can help improve the energy efficiency of the lightly loaded edge servers. Finally, we observe that, despite the presented benefits, in renewable energy powered MEC systems, ESI and CSI are still required for decision making; thus the MEC system must be able to acquire this information from the RNIS.