Adaptive Resource Management for a Virtualized Computing Platform within Edge Computing

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 Multiaccess 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.

Optimal Workload Allocation for Edge Computing Network Using Application Prediction

“…In 2019's section, many new strategies for reducing delay in resource allocation are also proposed. In [16], Dlamini and Gambin proposed an adaptive fog architecture, which dynamically selects different links to reduce transmission delay by measuring link delay. When the link delay is high, the performance is poor.…”

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

“…Here, a MEC server manages the SBSs powered by green energy. Similar to [11], forecasting and foresighted optimization are used for edge system management.…”

“…In this regard, it is worth noting that the energy consumption within the virtualized computing platform is due to (i) the active computing resources, i.e., VM or containers [7][8][9], and (ii) the network communications, communication related to transmission drivers [10,11], and the intracommunications [12]. In order to alleviate this, this paper assumes that the container-based virtualization be deployed in the MEC server as containers are lightweight, i.e., demand less memory space, have shorter start-up time, and offer software portability.…”

“…This work is an extension of [11], where energy savings and QoS guarantee were considered only within a virtualized computing platform placed in proximity to a cluster of BS. In [11], the role of the MEC server is to handle the offloaded computational workload only, which means that the energy cost model lacks the consideration of the BS management procedures, caching process, and the use of containers.…”

Softwarization in Future Mobile Networks and Energy Efficient Networks

“…The virtualized network infrastructure powered by hybrid energy sources: On-grid power and green energy. The electro-mechanical switch (SW) selects the appropriate source of energy [26]. containers), as the computing resources, empowered with EH capabilities through a solar panel and an energy buffer (EB) that enables energy storage.…”