Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

Abstract

The convergence of communication and computing has lead to the emergence of Multi-access Edge Computing (MEC), where computing resources (supported by Virtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN), i.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with Energy Harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called ENergy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and QoS provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

Softwarization in Future Mobile Networks and Energy Efficient Networks

“…In [35], the network impact is used to identify the BS to be switched off within a cluster, one at a time, with no significant network performance degradation. Moreover, the network impact has been used in [36] to identify a BS to be switched off within a BS cluster where each BS is empowered with computation capabilities. With the advent of EH, it is desirable to incorporate the green energy utilization as a performance metric in traffic load balancing strategies [37,38].…”

“…In [17], a dynamic resource provisioning framework for a virtualized computing environment is presented and it is experimentally validated on a small server cluster that provides online services. Along the lines of MEC [28,36], the long-term short memory (LSTM) neural network is used for forecasting the traffic load and harvested energy, and then the limited lookahead control (LLC) technique uses the forecasted traffic load and harvested energy to obtain the best control input that will drive the edge systems into the desired behavior.…”

Adaptive Resource Management for a Virtualized Computing Platform within Edge Computing

“…In [6], computing resources are provisioned depending on the expected server workloads via a reinforcement learning-based resource management algorithm, which learns the optimal policy for dynamic workload offloading and servers autoscaling. Our previous works in [7] and [17], focus on the provision of computing resources (VMs) based on a Limited Lookahead Control (LLC) policy and the network impact (the use of traffic load as a performance metric [18]), after forecasting the future workloads and harvested energy. A single Base Station (BS) optimization case is considered for an off-grid site in [7], and a multiple BS optimization case, each BS site powered by hybrid energy sources, is studied in [17] where the edge management procedures are enabled by an edge controller.…”

“…Our previous works in [7] and [17], focus on the provision of computing resources (VMs) based on a Limited Lookahead Control (LLC) policy and the network impact (the use of traffic load as a performance metric [18]), after forecasting the future workloads and harvested energy. A single Base Station (BS) optimization case is considered for an off-grid site in [7], and a multiple BS optimization case, each BS site powered by hybrid energy sources, is studied in [17] where the edge management procedures are enabled by an edge controller. This work differs from our previous works as the MEC server is placed in proximity to a BS cluster, and not one co-located for each BS.…”

Overview on 5G Radio Frequency Energy Harvesting

“…An example considers BSs equipped with EH capabilities and may reduce thus by the same occasion energy consumption. The authors in [109] suggest to optimize the BSs and virtual MEC resources use following traffic load and forecasted harvested energy parameters. The heuristic optimization employs sleep mode for BS and softscaling for Virtual machines.…”