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Dataset . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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https://doi.org/10.5281/zenodo...
Dataset . 2019
License: CC BY
Data sources: Sygma
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Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds

Authors: Estévez-Caldas, Alberto; García-Rois, Juan; Gil-Castiñeira, Felipe; Candal-Ventureira, David; Fondo-Ferreiro, Pablo; González-Castaño, Francisco J.;

Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds

Abstract

The main objective of the DYNAMO Fed4FIRE+ experiment was to perform Network Function Virtualization (NFV) Management and Network Orchestration (MANO) of a cellular network on top of cloud infrastructures, exploring one of the key enabling technologies for 5G systems and beyond. DYNAMO used cloud and radio access facilities at the IRIS testbed and cloud facilities at the University of Vigo (UVIGO) to deploy an end-to-end (E2E) cellular network and perform elastic changes on it if needed. The geographic distance in between facilitated the setup of a realistic Multi-Access Edge Computing (MEC) use case, where the virtual Evolved Packet Core (vEPC) was deployed at UVIGO (Spain) and the access network, i.e., the User Equipment (UE), the e-Node-B (eNB) and edge cloud, were implemented on IRIS testbed (Ireland). While the initial deployment of the E2E cellular network may be considered as static, DYNAMO showcases the elasticity that an E2E cellular network may need in runtime. Hence, we presented a use case consisting of a latency sensitive E2E cellular network (network slice), where the endpoint of the UE connection was initially located in the core (UVIGO) but then migrated to the edge (IRIS), in case the UE's latency ranges were unacceptable. In this regard, the UE reported the experienced latency to Open Network Automation Platform (ONAP), which is responsible to trigger specific policy-driven control actions if a predefined Service-Level Agreement (SLA) is violated. This datased includes the reports provided by the UE to ONAP. As a result, the endpoint of the data plane of the UE is automatically moved to the access network (IRIS) thus reducing significantly the latency for the UE. For the access part of the network, we implemented one srsLTE e-Node-B (eNB), one srsLTE User Equipment (UE) and a Devstack (Edge Cloud) in virtual machines on IRIS testbed. In addition, we also implemented an SDN switch controlled by an ONOS SDN controller. For the core part of the network, we considered a disaggregated vEPC from Open Air Interface (OAI) on a Devstack (Core Cloud) at UVIGO. DYNAMO has succeeded in the integration of a broad set of network elements and technologies between the two different domains (UVIGO and IRIS testbed) and fulfilled all initial objectives: (i) establishing communication between ONAP and IRIS testbed to deploy generic VNFs on core and edge clouds, (ii), deployment of an E2E cellular network with UE and eNB in IRIS and the vEPC at UVIGO, (iii), sending telemetry of the UE to ONAP and (iv) designing and testing closed-loop control actions in ONAP to migrate the data plane of the UE to the edge in case of unsatisfactorily SLA. DYNAMO paves the way to a broad set of future 5G experiments that will require resource orchestration, such as the deployment of network slices or the automatic scheduling of services in the limited resources of Edge Clouds. This repository contains the information sent from the UE to ONAP, in order to decide if the latency between the UE and the PGW is OK or if an action has to be considered to reduce such latency.

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Keywords

SPGW relocation, UE-SPGW, latency

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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