# Adaptive Resource Management for a Virtualized Computing Platform within Edge Computing

## TL;DR

This paper introduces ARCES, an online resource management algorithm for edge computing that minimizes energy consumption by jointly optimizing computing and communication resources under QoS constraints.

## Contribution

It proposes a novel energy-aware server management algorithm that considers both computing and communication energy, integrating traffic forecasting, workload allocation, and energy harvesting in edge environments.

## Key findings

- ARCES achieves up to 69% energy savings.
- Energy consumption reduced to 31%-45% of baseline.
- Effective under various reconfiguration costs.

## Abstract

In virtualized computing platforms, energy consumption is related to the computing-plus-communication processes. However, most of the proposed energy consumption models and energy saving solutions found in literature consider only the active Virtual Machines (VMs), thus the overall operational energy expenditure is usually related to solely the computation process. To address this shortcoming, in this paper we consider a computing-plus-communication energy model, within the Multi-access Edge Computing (MEC) paradigm, and then put forward a combination of a traffic engineering- and MEC Location Service-based online server management algorithm with Energy Harvesting (EH) capabilities, called Automated Resource Controller for Energy-aware Server (ARCES), for autoscaling and reconfiguring the computing-plus-communication resources. The main goal is to minimize the overall energy consumption, under hard per-task delay constraints (i.e., Quality of Service (QoS)). ARCES jointly performs (i) a short-term server demand and harvested solar energy forecasting, (ii) VM soft-scaling, workload and processing rate allocation and lastly, (iii) switching on/off of transmission drivers (i.e., fast tunable lasers) coupled with the location-aware traffic scheduling. Our numerical results reveal that ARCES achieves on average energy savings of 69%, and an energy consumption ranging from 31%-45%and from 21%-25% at different values of per-VM reconfiguration cost, with respect to the case where no energy management is applied.