A Hierarchical MEC Architecture: Experimenting the RAVEN Use-Case

Sabella, Dario; Nikaein, Navid; Huang, Anta; Xhembulla, Jetmir; Malnati, Giovanni; Scarpina, Salvatore

doi:10.1109/vtcspring.2018.8417826

Cited by 22 publications

(10 citation statements)

References 3 publications

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“…The use of RNIS in an as-a-service manner has been evaluated in [14], with an implementation of a publish-subscribe scheme based on RabbitMQ that is suitable for real-time use-cases. For instance, using a similar approach, the ETSI RAVEN POC [15] showcased a video optimization MEC application that dynamically adjusts the quality of the video streams according to the radio conditions of the users. Similarly, our MELD plugin allows picoquic to exploit relevant radio information (i.e., CQI, TBS and TxQueue length) in order to differentiate at the transport layer congestion losses from random transmission losses.…”

Section: B Rnis As a Servicementioning

confidence: 99%

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks

Diarra

Dabbous

Ismail

et al. 2021

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

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Traditional loss-based Congestion Control Algorithms (CCAs) suffer from performance issues over wireless networks mostly because they fail to distinguish wireless random losses from congestion losses. Different loss discrimination algorithms have been proposed to tackle this issue but they are not efficient for 4G networks since they do not consider the impact of various link layer mechanisms such as adaptive modulation and coding and retransmission techniques on congestion in LTE Radio Access Networks (RANs). We propose MELD (MEC-based Edge Loss Discrimination), a novel server-side loss discrimination mechanism that leverages recent advancements in Multi-access Edge Computing (MEC) services to discriminate packet losses based on real-time RAN statistics. Our approach collects the relevant radio information via MEC's Radio Network Information Service and uses it to correctly distinguish random losses from congestion losses. Our experimental study made with the QUIC transport protocol shows over 80% higher goodput when MELD is used with New Reno and 8% higher goodput when used with Cubic.

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Section: B Rnis As a Servicementioning

confidence: 99%

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks

Diarra

Dabbous

Ismail

et al. 2021

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

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“…RAPID is a TCP proxy that seats close to the base station, in the MEC host and that leverages up-to-date radio information exposed by the Radio Network Information Service (RNIS) [9], similar to RAVEN [28] and MELD (proposed in our previous work [7]). The use of RNIS in real-world experimentation has been demonstrated by FlexRAN [10], an open-source implementation of RNIS which allows a controller deployed somewhere in the network to collect radio information from some agents collocated with the deployed base stations.…”

Section: Proxy Architecturementioning

confidence: 99%

RAN-aware Proxy-based Flow Control for High Throughput and Low Delay eMBB

Diarra

Dabbous

Ismail³

et al. 2021

Proceedings of the 24th International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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“…Additionally, Sabella et al suggested a hierarchical MEC architecture for adaptive video streaming in V2X applications. 25 The authors described the acquisition of real-time channel data by local agents stationed at the evolved NodeB (eNB). This information is then communicated to a MEC platform, which automatically changes the video stream's quality to match the channel's conditions.…”

Section: Applicability Of Ec For V2xmentioning

confidence: 99%

Edge computing for Vehicle to Everything: a short review

et al. 2021

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Vehicle to Everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. V2X serves to organise communication and interaction between vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrians (V2P), and vehicle to networks (V2N). However, having multiple communication channels can generate a vast amount of data for processing and distribution. In addition, V2X services may be subject to performance requirements relating to dynamic handover and low latency communication channels. Good throughput, lower delay, and reliable packet delivery are the core requirements for V2X services. Edge Computing (EC) may be a feasible option to address the challenge of dynamic handover and low latency to allow V2X information to be transmitted across vehicles. Currently, existing comparative studies do not cover the applicability of EC for V2X. This review explores EC approaches to determine the relevance for V2X communication and services. EC allows devices to carry out part or all of the data processing at the point where data is collected. The emphasis of this review is on several methods identified in the literature for implementing effective EC. We describe each method individually and compare them according to their applicability. The findings of this work indicate that most methods can simulate the EC positioning under predefined scenarios. These include the use of Mobile Edge Computing, Cloudlet, and Fog Computing. However, since most studies are carried out using simulation tools, there is a potential limitation in that crucial data in the search for EC positioning may be overlooked and ignored for bandwidth reduction. The EC approaches considered in this work are limited to the literature on the successful implementation of V2X communication and services. The outcome of this work could considerably help other researchers better characterise EC applicability for V2X communications and services.

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A Hierarchical MEC Architecture: Experimenting the RAVEN Use-Case

Cited by 22 publications

References 3 publications

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks

RAN-aware Proxy-based Flow Control for High Throughput and Low Delay eMBB

Edge computing for Vehicle to Everything: a short review

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