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

## TL;DR

This paper introduces ENAAM, an online control algorithm for energy-efficient management of energy harvesting base stations with computing capabilities, improving energy savings and QoS in mobile networks.

## Contribution

It proposes a foresighted online optimization algorithm for dynamic energy and resource management in energy harvesting base stations with computation capabilities.

## Key findings

- ENAAM achieves 57-69% energy savings over no management.
- Extending ENAAM to BS clusters yields an additional 9-16% savings.
- The algorithm effectively balances energy efficiency and QoS.

## 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 in 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.