MEC-based Stable Topology Prediction for Vehicular Networks

Use Case Details:

Attributes	Description	Informational
Type	New	The use case is proposed under the ICN BP family
Industry sector	Area: SDN & NFV Company: ATTO Research University: Jeju National University Country: Republic of Korea	ATTO RESEARCH focuses on the problems related to networking and software technology for a better connection. It has created a technology that allows to build IT infrastructure, and aims to grow into a ‘Software Defined Infrastructure’ company.
Business driver	The stable vehicular network is essential to enable various applications such as autonomous driving through VANETs. Proposed MEC architecture tends to enable a promising infrastructure where a stable network topology can be predicted locally to improve the network performance by providing intensive calculation for vehicles in the adjacent roads. Thus, converging the two concepts of MEC and topology prediction can provide a strong use case for the vehicular networks such as proactive path stabilization.	The MEC-based Efficient Routing Algorithm 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 from being useful to provide a stable topology on the road tracks.
Business use case	Edge cloud deployable at RSUs to support applications such as ML-based location prediction, topology stabilization
Business cost - Initial build	Minimal configuration is three servers in total: Master/Database node (1^st server) Edge node 1 (2^nd server) Edge node 2 (3rd server)	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 name	MEC-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 One Orchestration node Three Edge nodes Jenkins for Continuous Integration and Continuous Delivery Personal servers will be integrated with the Linux Foundation servers

Contributors:

Name	Company	Email (Contact)	Roles
MalikAsif	Jeju National University	malikasifmahmoodawan@gmail.com	R&D, PTL
saqib haleem	Jeju National University	em.saqb@gmail.com	R&D
@Muhammad Ali Jibran	---	alijibran35@gmail.com	R&D
Afaq Muhammad	Jeju National University	afaq24@gmail.com	Supervisor
Wang-Cheol Song	Jeju National University	philo@jejunu.ac.kr	Supervisor
@Taekyung Lee	ATTO Research	taekyung.lee@atto-research.com	Partner

References:

#	Relevance	Title
1	IoV, Prediction	Abbas, M.T., Muhammad, A. and Song, W.C., 2019. Road-aware estimation model for path duration in Internet of vehicles (IoV). Wireless Personal Communications, 109(2), pp.715-738.
2	IoV, Prediction	Abbas, M.T., Jibran, M.A., Afaq, M. and Song, W.C., 2020. An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter. Transactions on Emerging Telecommunications Technologies, 31(5), p.e3734.
3	IoV, Prediction	Abbas, M.T., Muhammad, A. and Song, W.C., 2020. SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach. Journal of Ambient Intelligence and Humanized Computing, 11(3), pp.1265-1280.
4	RSU, MEC	Wang, X., Han, Y., Leung, V.C., Niyato, D., Yan, X. and Chen, X., 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), pp.869-904.
5	RSU, MEC	Ndikumana, A., Tran, N.H., Kim, K.T. and Hong, C.S., 2020. Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing. IEEE Transactions on Intelligent Transportation Systems.

Tasks (for the team):

Task name	Details	Creation date	Completion date	Assignee
Voting for BP	Request and follow up voting process.	21 Sep 2020	22 Sep 2020	MalikAsif
Project Approved: 22 Sep 2020
Request for PTL	Request to initiate the PTL.	01 Oct 2020	01 Oct 2020	MalikAsif
MalikAsifor saqib haleemgot elected as PTL: 08 Oct 2020
Setup PM	Setup the Physical Machine.	01 Oct 2020	05 Oct 2020	MalikAsif saqib haleem
Setup Jenkins	Setup Jenkins on PM.	06 Oct 2020	06 Oct 2020	MalikAsif saqib haleem
Connect LF Server	Integrate the Linux Foundation Servers.	07 Oct 2020	07 Oct 2020	saqib haleem
Push CICD logs	Confirmation of the CICD logs	08 Oct 2020	09 Oct 2020	saqib haleem
Explore/Research K8s container protocol options in python	Explore the containers inter/cross domain protocol options to enable communication among each other.	01 Oct 2020	09 Oct 2020	saqib haleem MalikAsif
Explore/Research K8s network options	Explore the networking plugin options that can be used in our proposed scenarios of vehicles.	01 Oct 2020	08 Oct 2020	MalikAsif saqib haleem
Create documentation pages & subsections	Architecture - figures/explanation Components - figures/explanation Use-cases/Scenarios - figures/explanation Implementation - plan/approach Ask Questions - toq	01 Oct 2020	13 Oct 2020	MalikAsif saqib haleem
Create vehicle containers	Vehicle Classes	02 Oct 2020	12 Oct 2020	MalikAsif
Maps	Search ways for creating roads, intersection etc Implement roads. intersection etc	08 Oct 2020	31 Oct 2020	MalikAsif

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 Info	Time Zone	Committer Bio	Committer Picture	Self Nominate for PTL (Y/N)
MalikAsif	Jeju National University	malikasifmahmoodawan@gmail.com	Asia/Seoul (UTC+9)			Y
saqib haleem	Jeju National University	em.saqb@gmail.com	Asia/Seoul (UTC+9)

Motivation:

Motivational aspects are:

The topology in a vehicular network is updated and retrieved frequently
- This causes path instability
Vehicular networks are wireless in nature
- However, Software-defined networking (SDN) is originally designed for wired networks
Leads to the need for topology stability in vehicular networks

To this end, we introduce:

Computation at the Edge
Topology prediction to proactively stabilize the paths in vehicular network
Proximity Services

Motivational Idea

Architecture Overview:

The architectural view of our system is as follows:

Use case Attributes:

Stable network topology in IoV
Road aware, predictive, and proactive connection

2. Road aware, predictive and proactive connection

Stable Path Scenarios:

Stable path connectivity scenarios are illustrated as shown below:

The below figure (shown left side) is a scenario where the devices are under same cell and in coverage, which want to communicate. The below figure (shown right side) is a scenario where the devices are under same cell and one oft them is out of coverage, which want to communicate.

The below figure explores the Device to Device communication where a device 1 wants to communicate device-4 and both of them reside under a different Cell node. In this case, the packet is forwarded from one edge vEPC to another. This way the connectivity is provided as follows:

The below scenario explains the same as immediate previous scenario but the only difference among them is that one of the device is out of coverage and proximity service plays the role of provisioning the location of such devices.

The below figure shows the direct communication between the devices which reside under different Cells. The below path was provided as a result of proactive approach that we propose in our system. The major difference between the below two scenarios is that in such cases, we don't require the involvement of Edge vEPC in order to forward the packet to another road segment, thus reducing the latency and improving the performance in terms of bandwidth and efficient resource usage of the cellular spectrum. Both the devices are in coverage.

The below figure also shows the direct communication between the devices which reside under different Cells. The below path was provided as a result of proactive approach that we propose in our system. The difference from previously mentioned scenario is that one of these devices are out of coverage. In this case also, the benefits to reduce the latency and efficient resource usage were achieved.

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