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rootMEC

Edge Computing

-based Stable Topology Prediction for Vehicular Networks

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Integration Projects (aka "Blueprints")

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MEC-based Stable Topology Prediction for Vehicular Networks

Use Case Details:

Attributes

Description

Informational

TypeNewThe use case is proposed under the ICN BP family
Industry sector

Area: SDN & NFV


Company: ATTO Research


University:

Jeju National

Gachon University
Country: Republic of Korea

ATTO RESEARCH focuses
We focus on the problems related to networking and software technology for a better connection.
It has created a
The technology that allows to build IT infrastructure, and aims to grow into a ‘Software Defined Infrastructure’ company.
Business
driversIn order
driverThe stable vehicular network is essential to enable various applications such as autonomous driving through VANETs
, stable vehicular network is essential. Edge cloud proves to be
. Proposed MEC architecture tends to enable a promising infrastructure where a stable network topology can be predicted locally
in order
to improve the network performance by providing intensive calculation for vehicles in the adjacent roads. Thus, converging the two concepts of
edge computing
MEC and topology prediction can provide a strong use
-
case for the vehicular networks such as proactive path stabilization.The
position
MEC-based Efficient Routing Algorithm
is able to
can provide a stable path by using the predicted future position for the nearby vehicles. The information can also be made available to the adjacent road resulting
to be
from being useful to provide a stable topology on the road tracks.
Use cases

  • Stable network topology prediction in Internet of Vehicles (IoV) at the Edge cloud

  • QoS-based multi-channel support for different types of services

  • Real-time Computation for Autonomous and Self-driving cars

The topology prediction performed at the edge cloud will provide real-time network topology as well as a stable routing path in IoV.
Business use case
  • Edge cloud deployable at RSUs to support applications such as ML-based location prediction, topology stabilization

Business cost - Initial build

Minimal

Business cost (initial build cost target objective)The minimal

configuration is three servers in total:

  • Master/Database node (
1st
  • 1st server)
  • Edge node 1 (
2nd
  • 2nd server)
  • Edge node 2 (3rd server)
Scale & Type3 (x86 server)The physical machines are used for different purposes such as Master, Edge and Database nodes. 1st server will be used as a Master and Database node. The remaining two physical servers will be used as two different edge nodes for computing.Nodes (virtual)
  • 1 master node
  • 1 database node
  • 2 edge nodes
Our system has three basic nodes. Master node being the one to manage the other nodes helps out in scaling up the number of edge nodes and applications on them. Edge nodes have the prediction models which provide the network topology in IoV. Database node contains the information of each edge node accessible to the Master node via which Master node can also view the overall network topology in IoV for different purposes.ApplicationsApplications for autonomous vehicles such as topology and location prediction on edge clouds.These applications (being latency sensitive) can be extended to any type of business use-cases.
Price factor depends on the cost of RSU quality, and should be only considered for physical deployment. i.e. wireless or wired.
Business cost - Operational

Virtual environment does not require cost.


Operational need

Using the frontend GUI to:

  • Orchestrate virtual resources
Manage the edge applications
Additional details
  • Support of path within a Single operator domain

PPT is attached as proposal statement.

Species Details:

Attributes

Description

Informational

Type

Integrated Cloud Native NFV/App stack (ICN)


Blueprint Family

Existing


Use case

Stable network topology in IoV


Blueprint proposed nameMEC-based Stable Topology Prediction for Vehicular Networks

Initial POD cost

Satellite POD


Scale & Type

System will be developed/deployed in VMs.


Applications
  • ML model
  • LTE network services
  • ProSe Functions

Open Air Interface (OAI) provided LTE network services will be used.

Infrastructure orchestration

  • OpenStack latest/stable release – VM orchestration
  • Kubernetes-based container orchestration
  • WeaveNet -based Container Networking
  • VNF Orchestration – ONAP
  • OS – Ubuntu 18.X LTS
  • CICD - Jenkins 2.249.1 LTS

SDN

ONOS will be used at the application layer


Workload type

VMs and Containers


Additional

  • 4 Virtual Machines
    1. One Orchestration node
    2. Three Edge nodes
  • Jenkins for Continuous Integration and Continuous Delivery
  • Personal servers will be integrated with the Linux Foundation servers

Committers and PTL (Project Technical Lead)

Please enter in all names of the committers for the project.

PTL is done off of self nomination process.  If you wish to be considered for the PTL, please indicate that by putting a Y in the self nomination column (use the slide to move the table left to right).  Per Akraino rules, if there is only one nominee, that person becomes PTL (when confirmed by the Akraino TSC).  If there is more than nominee, we will then have an election.
The election process is open and will go through 7 Oct. 2020 at Noon Pacific time. 

Committer

Committer Company

 Committer Contact InfoTime Zone

Committer Bio

Committer Picture

Self Nominate for PTL (Y/N)

Asif Mehmood

Gachon University

malikasifmahmoodawan@gmail.com

Asia/Seoul (UTC+9)

Image AddedGithub Logo - Free social media iconsImage AddedImage AddedImage AddedDocker Logos - DockerImage Added

Image Added

Y

First (1st)Second (2nd)

from:

to:

from:

to: Present


Faisal Mehmood 

Gachon University


Asia/Seoul (UTC+9)

from:  

to: Present























Infrastructure orchestration
  • OSM (Orchestrator)
  • OpenStack (cloud provider)
  • ONOS (SDN controller)
  • OpenFlow (SDN southbound protocol)
  • Ubuntu 18.04.3 LTS (OS)
Workload typeAdditional detailsSummary/ Overview

Due to recent advancement in networks, IoV has a major role to play and is critical to performance. To this end, we introduce machine learning mechanisms integrated at the edge cloud as a proposal. As it is a known fact that the vehicles at the edge are directly connected to the Radio Station Units/Base Stations (RSU/BS) and placing such application/software packages at the edge can significantly enhance the performance in the IoV domain.

QoS-based multi-channel ScenarioImage Removed

Further, the extended version of Kalman Filter is proposed to predict the network topological locations. The inference engine used for predicting the vehicular locations and further forming them into predicted topology proves to provide a stable path for the vehicles to communicate on the provided flow-based channels. Overall, the solution and architecture are flexible in nature which further allow the support of multiple use cases in 5G.


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