Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud

Depasquale, Etienne-Victor; Davoli, Franco; Rajput, Humaira

doi:10.3390/s23010255

Cited by 5 publications

(5 citation statements)

References 94 publications

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“…In [2] an overview is presented summarizing the challenges, approaches and results of the state-of-the art research concerning the empirical measurements and analytical modelling of virtual entities power consumption in the telco cloud.…”

Section: A Power Consumption Measurements and Modellingmentioning

confidence: 99%

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Building the Digital Twin of a MEC node: a Data Driven Approach

Fedrizzi

Bellin

Costa

et al. 2023

2023 IEEE 9th International Conference on Network Softwarization (NetSoft)

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Multi-access edge computing (MEC) represents an emerging solution to improve the performance of mobile networks by bringing computing resources closer to the edge of the network. However, MEC requires the implementation of virtualization and can be deployed using different hardware platforms, including COTS devices. In this highly heterogeneous scenario, the digital twin (DT), assisted by proper AI/ML solutions, is envisioned to play a crucial role in automated network management, operating as an intermediate and collaborative layer enabling the orchestration layer to better understand network behavior before making changes to the physical network. In this paper, we aim to develop a DT model that captures the behavior of a MEC node supporting services with varying workloads. In pursuit of this objective, we adopt a data-driven methodology that effectively learn a model predicting three critical key performance indicators (KPIs): throughput, computational load, and power consumption.To demonstrate the viability and potential of such approach, a measurement campaign is conducted on MEC nodes deployed with different virtualization environments (bare metal, virtual machine, and containerized), and the results are used to build the DT of each node. Furthermore, machine learning models, including k-nearest neighbors (KNN), support vector regression (SVR), and polynomial fitting (PF), are used to understand the amount of actual measurements required to achieve a suitably low KPI prediction error. The results of this study provide a basis for further research in the field of MEC DT models and carbon footprint-aware orchestration.

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Section: A Power Consumption Measurements and Modellingmentioning

confidence: 99%

“…The RAN is established with the use of UERANSIM 2 which is an emulator designed to simplify the deployment of an E2E 5G network. Upon initiation, the user equipment (UE) process effectively establishes a data-plane connection between the UE and the selected Open5GS deployment.…”

Section: A Testbed Setupmentioning

confidence: 99%

Building the Digital Twin of a MEC node: a Data Driven Approach

Fedrizzi

Bellin

Costa

et al. 2023

2023 IEEE 9th International Conference on Network Softwarization (NetSoft)

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“…The topic of measuring and modelling of power consumption in virtualized environment is not a new one and some heterogeneous works already exists in the literature. The survey in [5] identify seven dimensions of variability in this research space and proceed to give an exhaustive overview of the different challenges and approaches present in the current state-of-the-art.…”

Section: Related Workmentioning

confidence: 99%

“…While multiple studies have analyzed the performance and power consumption overhead of virtualization for the generic datacenter and cloud computing environment, "[...] the impact of virtualization technologies on power consumption in public telecommunication networks (PTNs) is still unclear" [5].…”

Section: Related Workmentioning

confidence: 99%

“…The power consumption layer represents the software and hardware that we propose to integrate in the network infrastructure in order to monitor the power consumption. In the case of the testbed used in this paper, each of the three NUC running the 5GC instances is connected to a Meross MSS310 5 smart plug capable of monitoring its power consumption in real-time. Thanks to open-source community projects which provide interesting insights about the Meross protocol 67 , we were able to put in place a metrics retrieval system by which the smart plugs can be queried locally using specifically crafted HTTP packets.…”

Section: B Power Consumption Layer Setupmentioning

confidence: 99%

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A Preliminary Study on the Power Consumption of Virtualized Edge 5G Core Networks

Bellin

Centenaro²,

Granelli

2023

2023 IEEE 9th International Conference on Network Softwarization (NetSoft)

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Other than pure performance and cybersecurity, a value that is becoming increasingly important for a mobile network is its power consumption. In fact, the transition from legacy network deployments tightly coupled with the underlying hardware towards fully virtualized ones yields distinct options based on the adopted virtualization technology, each of which deserve appropriate evaluation in terms of energy efficiency. In this paper, we aim at providing a preliminary assessment of the realistic power consumption of a fifth-generation core network deployed in a network edge environment leveraging bare metal, containers, and virtual machines. The results are based on a testbed consisting of commercial off-the-shelf hardware and open-source software, and show that the deployment based on virtual machines is the first one that saturates the power consumption, thus reducing the maximum achievable throughput. These preliminary insights show the feasibility of a real-time power monitoring system that can condition the dynamic policies applied by the 5G network orchestrator.

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