Hack2022 2nd Prize - Revision history

Velez at 09:10, 28 March 2024

2024-03-28T09:10:08Z

← Older revision		Revision as of 10:10, 28 March 2024
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			<p class="left" style="font-size:26px;"> ←''[[ETSI_-_LF_MEC_Hackathon_2022\|MEC Hackathon 2022]]''<p>
			<br>
	{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}		{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}

Velez at 10:13, 24 November 2022

2022-11-24T10:13:06Z

← Older revision		Revision as of 11:13, 24 November 2022
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	= Team=		= Team=
			[[File:hack2022-pedraforca_team.png\|600px\|center\|top\|class=img-responsive]]
			<br>
	<div class="flex-row row">		<div class="flex-row row">

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	* Sarang Kahvazadeh, CTTC		* Sarang Kahvazadeh, CTTC
	* Sergio Barrachina-Muñoz, CTTC		* Sergio Barrachina-Muñoz, CTTC
	~~[[File:hack2022-pedraforca_team.png\|400px\|center\|top\|class=img-responsive]]~~
	</div>		</div>
	<!-- <div class="panel-footer">Footer text 1</div> -->		<!-- <div class="panel-footer">Footer text 1</div> -->
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	</div><!-- end of row 1-->		</div><!-- end of row 1-->


	= Introduction =		= Introduction =

Velez at 10:07, 24 November 2022

2022-11-24T10:07:29Z

@@ Line 102: / Line 102: @@
 <br>
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 = Project Videos =

Velez at 09:58, 24 November 2022

2022-11-24T09:58:18Z

← Older revision		Revision as of 10:58, 24 November 2022
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	* Sarang Kahvazadeh, CTTC		* Sarang Kahvazadeh, CTTC
	* Sergio Barrachina-Muñoz, CTTC		* Sergio Barrachina-Muñoz, CTTC
			[[File:hack2022-pedraforca_team.png\|400px\|center\|top\|class=img-responsive]]
	</div>		</div>
	<!-- <div class="panel-footer">Footer text 1</div> -->		<!-- <div class="panel-footer">Footer text 1</div> -->

Velez at 08:54, 24 November 2022

2022-11-24T08:54:32Z

← Older revision		Revision as of 09:54, 24 November 2022
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	* Michail Dalgitsis, Vicomtech		* Michail Dalgitsis, Vicomtech
	* Sarang Kahvazadeh, CTTC		* Sarang Kahvazadeh, CTTC
	* Sergio Barrachina, CTTC		* Sergio Barrachina-Muñoz, CTTC

	</div>		</div>

Velez at 16:35, 22 November 2022

2022-11-22T16:35:38Z

Velez at 16:31, 22 November 2022

2022-11-22T16:31:45Z

← Older revision		Revision as of 17:31, 22 November 2022
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	<div class="panel panel-default">		<div class="panel panel-default">
	<div class="panel-body">		<div class="panel-body">
	Team '''Pedraforca'''		Team '''Pedraforca'''
	* Rasoul Nikbakht Silab, CTTC (Centre Tecnològic de Telecomunicacions de Catalunya)		from ''' CTTC ''' (Centre Tecnològic de Telecomunicacions de Catalunya) and ''' Vicomtech '''
			* Rasoul Nikbakht Silab, CTTC
	* Michail Dalgitsis, Vicomtech		* Michail Dalgitsis, Vicomtech
	* Sarang Kahvazadeh, CTTC		* Sarang Kahvazadeh, CTTC

Velez at 16:30, 22 November 2022

2022-11-22T16:30:00Z

← Older revision		Revision as of 17:30, 22 November 2022
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	= Experimental scenario =		= Experimental scenario =
	<p class="center" style="font-size:~~28px~~;"><b>a MEC-enabled cloud-native 5G network with over-the-air transmissions</b><p>		<p class="center" style="font-size:22px;"><b>a MEC-enabled cloud-native 5G network with over-the-air transmissions</b><p>

	<br>		<br>

	== Infrastructure (core, edge and RAN) ==		== Infrastructure (core, edge and RAN) ==
	The experimental scenario will consist of a 5G network spanning three domains: core (at the cloud), edge (realized as MEC), and RAN (provided by a commercial gNB). The core and edge domains will consist of virtual machines acting as commercial/consumer off-the-shelf (COTS) servers and the virtual networks to interconnect them, so to provide computation/storage and networking resources, respectively. The RAN will be realized using the Amarisoft Callbox as gNB.		* The experimental scenario will consist of a 5G network spanning three domains: core (at the cloud), edge (realized as MEC), and RAN (provided by a commercial gNB).
	[[File:Hack2022-pedraforca-architecture.png\|~~400px~~\|center\|top\|class=img-responsive]]
			* The core and edge domains will consist of virtual machines acting as commercial/consumer off-the-shelf (COTS) servers and the virtual networks to interconnect them, so to provide computation/storage and networking resources, respectively. The RAN will be realized using the Amarisoft Callbox as gNB.
			[[File:Hack2022-pedraforca-architecture.png\|600px\|center\|top\|class=img-responsive]]

	== Kubernetes / Cloud-native ==		== Kubernetes / Cloud-native ==
	Our deployment will have a cloud-native flavor, hence both the network functions (NFs) in the core and the MEC applications running on the edge will be realized as dockerized containers. The 5G core NFs will be based on the Open5GS open-source project. Remarkably, each NF will run as a separate containerized NF (CNF). To that aim, we will rely on Kubernetes for easing the orchestration of a cluster composed of the core and edge nodes. Further, in order to enable MEC capabilities, we will i) deploy UPF(s) at the edge, and ii) develop and implement a MEC platform (MEP) to pull metrics from the gNB so to provide the Radio Network Information Service (RNIS).		* Our deployment will have a cloud-native flavor, hence both the network functions (NFs) in the core and the MEC applications running on the edge will be realized as dockerized containers.

			* The 5G core NFs will be based on the Open5GS open-source project. Remarkably, each NF will run as a separate containerized NF (CNF). To that aim, we will rely on Kubernetes for easing the orchestration of a cluster composed of the core and edge nodes. Further, in order to enable MEC capabilities, we will i) deploy UPF(s) at the edge, and ii) develop and implement a MEC platform (MEP) to pull metrics from the gNB so to provide the Radio Network Information Service (RNIS).


	== Integration of MEC API with Amarisoft RAN ==		== Integration of MEC API with Amarisoft RAN ==
	ETSI MEC offers a plethora of open APIs to enable communication with edge and network resources. The one which is more related to interact with our 5G network implementation is the MEC012 RNIS API. Specifically, for the RAN part we will be working with a commercial solution such as the Amarisoft gNB, in which the connected users will access the MEC applications. Therefore, an integration between Amarisoft RAN and RNIS is needed.		* ETSI MEC offers a plethora of open APIs to enable communication with edge and network resources. The one which is more related to interact with our 5G network implementation is the MEC012 RNIS API.
	More specifically, the interaction between the user equipment or the 5G network administrators with Amarisoft is done through a proprietary WebSocket API. Thus, one of our main contributions will be to map WebSocket actions to the RNIS API, in order to extract all the appropriate information for the users and their QoS requirements.
			* Specifically, for the RAN part we will be working with a commercial solution such as the Amarisoft gNB, in which the connected users will access the MEC applications. Therefore, an integration between Amarisoft RAN and RNIS is needed.

			* More specifically, the interaction between the user equipment or the 5G network administrators with Amarisoft is done through a proprietary WebSocket API. Thus, one of our main contributions will be to map WebSocket actions to the RNIS API, in order to extract all the appropriate information for the users and their QoS requirements.

	== Deployment of virtual classroom application ==		== Deployment of virtual classroom application ==
	Akraino virtual classroom blueprint is a classroom app simulator that provides a virtual reality classroom with teachers and students. In this simulated app, teachers and students can interact with each other.		* Akraino virtual classroom blueprint is a classroom app simulator that provides a virtual reality classroom with teachers and students. In this simulated app, teachers and students can interact with each other.
	The main components of Akraino virtual classroom blueprint are Jenkins Master, Tars Master, and Tars Agent with AR/VR client
	[[File:Hack2022-pedraforca-virtualclass.png\|~~400px~~\|center\|top\|class=img-responsive]]		* The main components of Akraino virtual classroom blueprint are Jenkins Master, Tars Master, and Tars Agent with AR/VR client
			[[File:Hack2022-pedraforca-virtualclass.png\|600px\|center\|top\|class=img-responsive]]


	These components will be dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.		* These components will be dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.


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	''' ML-based control of the RAN and edge infrastructure using the data collected from MEC APIs '''		''' ML-based control of the RAN and edge infrastructure using the data collected from MEC APIs '''
	<br>		<br>
	MEC APIs collect information about different components and parameters of the 5G networks. Using these APIs and ML-based tools, we can make a decision engine that monitors/adjusts the RAN and edge infrastructures to meet required KPIs or scale the deployed applications.		* MEC APIs collect information about different components and parameters of the 5G networks. Using these APIs and ML-based tools, we can make a decision engine that monitors/adjusts the RAN and edge infrastructures to meet required KPIs or scale the deployed applications.
	Regarding the RAN part, we evaluate the user latency and bandwidth and use RNIS API to adjust RAN parameters based on the service requirements. For instance, this setting can
	guarantee the students in the virtual classroom have sufficient bandwidth for streaming and low latency for interacting with classmates and teachers.		* Regarding the RAN part, we evaluate the user latency and bandwidth and use RNIS API to adjust RAN parameters based on the service requirements. For instance, this setting can guarantee the students in the virtual classroom have sufficient bandwidth for streaming and low latency for interacting with classmates and teachers.
	Regarding the edge infrastructure, other ETCI MEC APIs can be used for scaling the services and applications. We deploy ML-based tools to predict the demand for edge applications and scale up and down the services accordingly. In the case of the virtual classroom, each classroom could be seen as a different pod, and virtualized hardware resources can be allocated based on the classroom instance and the number of students.
	Due to the limited resources available in the edge servers and 5G cells, the optimum resource allocation becomes a critical task. As a result, we plan to use different analytical and data-driven techniques to handle the resources in an (close to) optimal way.		* Regarding the edge infrastructure, other ETCI MEC APIs can be used for scaling the services and applications. We deploy ML-based tools to predict the demand for edge applications and scale up and down the services accordingly. In the case of the virtual classroom, each classroom could be seen as a different pod, and virtualized hardware resources can be allocated based on the classroom instance and the number of students.

			* Due to the limited resources available in the edge servers and 5G cells, the optimum resource allocation becomes a critical task. As a result, we plan to use different analytical and data-driven techniques to handle the resources in an (close to) optimal way.
	<!--		<!--

Velez at 16:23, 22 November 2022

2022-11-22T16:23:17Z

← Older revision		Revision as of 17:23, 22 November 2022
Line 56:		Line 56:
	== Infrastructure (core, edge and RAN) ==		== Infrastructure (core, edge and RAN) ==
	The experimental scenario will consist of a 5G network spanning three domains: core (at the cloud), edge (realized as MEC), and RAN (provided by a commercial gNB). The core and edge domains will consist of virtual machines acting as commercial/consumer off-the-shelf (COTS) servers and the virtual networks to interconnect them, so to provide computation/storage and networking resources, respectively. The RAN will be realized using the Amarisoft Callbox as gNB.		The experimental scenario will consist of a 5G network spanning three domains: core (at the cloud), edge (realized as MEC), and RAN (provided by a commercial gNB). The core and edge domains will consist of virtual machines acting as commercial/consumer off-the-shelf (COTS) servers and the virtual networks to interconnect them, so to provide computation/storage and networking resources, respectively. The RAN will be realized using the Amarisoft Callbox as gNB.
			[[File:Hack2022-pedraforca-architecture.png\|400px\|center\|top\|class=img-responsive]]

	== Kubernetes / Cloud-native ==		== Kubernetes / Cloud-native ==
Line 68:		Line 69:
	Akraino virtual classroom blueprint is a classroom app simulator that provides a virtual reality classroom with teachers and students. In this simulated app, teachers and students can interact with each other.		Akraino virtual classroom blueprint is a classroom app simulator that provides a virtual reality classroom with teachers and students. In this simulated app, teachers and students can interact with each other.
	The main components of Akraino virtual classroom blueprint are Jenkins Master, Tars Master, and Tars Agent with AR/VR client		The main components of Akraino virtual classroom blueprint are Jenkins Master, Tars Master, and Tars Agent with AR/VR client
	[[File:Hack2022-~~Optare~~-virtualclass.png\|400px\|center\|top\|class=img-responsive]]		[[File:Hack2022-pedraforca-virtualclass.png\|400px\|center\|top\|class=img-responsive]]
	~~Hack2022-Optare-virtualclass.png~~

	These components will be dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.		These components will be dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.

Velez at 16:18, 22 November 2022

2022-11-22T16:18:42Z

← Older revision		Revision as of 17:18, 22 November 2022
Line 68:		Line 68:
	Akraino virtual classroom blueprint is a classroom app simulator that provides a virtual reality classroom with teachers and students. In this simulated app, teachers and students can interact with each other.		Akraino virtual classroom blueprint is a classroom app simulator that provides a virtual reality classroom with teachers and students. In this simulated app, teachers and students can interact with each other.
	The main components of Akraino virtual classroom blueprint are Jenkins Master, Tars Master, and Tars Agent with AR/VR client		The main components of Akraino virtual classroom blueprint are Jenkins Master, Tars Master, and Tars Agent with AR/VR client
			[[File:Hack2022-Optare-virtualclass.png\|400px\|center\|top\|class=img-responsive]]
			Hack2022-Optare-virtualclass.png

	These components will be dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.		These components will be dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.

← Older revision		Revision as of 17:35, 22 November 2022
Line 56:		Line 56:

	== Infrastructure (core, edge and RAN) ==		== Infrastructure (core, edge and RAN) ==
	* The experimental scenario ~~will consist~~ of a 5G network spanning three domains: core (at the cloud), edge (realized as MEC), and RAN (provided by a commercial gNB).		* The experimental scenario consists of a 5G network spanning three domains: core (at the cloud), edge (realized as MEC), and RAN (provided by a commercial gNB).

	* The core and edge domains ~~will consist~~ of virtual machines acting as commercial/consumer off-the-shelf (COTS) servers and the virtual networks to interconnect them, so to provide computation/storage and networking resources, respectively. The RAN ~~will be~~ realized using the Amarisoft Callbox as gNB.		* The core and edge domains consists of virtual machines acting as commercial/consumer off-the-shelf (COTS) servers and the virtual networks to interconnect them, so to provide computation/storage and networking resources, respectively. The RAN is realized using the Amarisoft Callbox as gNB.
	[[File:Hack2022-pedraforca-architecture.png\|600px\|center\|top\|class=img-responsive]]		[[File:Hack2022-pedraforca-architecture.png\|600px\|center\|top\|class=img-responsive]]

	== Kubernetes / Cloud-native ==		== Kubernetes / Cloud-native ==
	* Our deployment ~~will have~~ a cloud-native flavor, hence both the network functions (NFs) in the core and the MEC applications running on the edge ~~will be~~ realized as dockerized containers.		* Our deployment has a cloud-native flavor, hence both the network functions (NFs) in the core and the MEC applications running on the edge are realized as dockerized containers.

	* The 5G core NFs ~~will be~~ based on the Open5GS open-source project. Remarkably, each NF ~~will run~~ as a separate containerized NF (CNF). To that aim, we ~~will rely~~ on Kubernetes for easing the orchestration of a cluster composed of the core and edge nodes. Further, in order to enable MEC capabilities, we ~~will~~ i) deploy UPF(s) at the edge, and ii) develop and implement a MEC platform (MEP) to pull metrics from the gNB so to provide the Radio Network Information Service (RNIS).		* The 5G core NFs is based on the Open5GS open-source project. Remarkably, each NF runs as a separate containerized NF (CNF). To that aim, we relies on Kubernetes for easing the orchestration of a cluster composed of the core and edge nodes. Further, in order to enable MEC capabilities, we i) deploy UPF(s) at the edge, and ii) develop and implement a MEC platform (MEP) to pull metrics from the gNB so to provide the Radio Network Information Service (RNIS).


Line 70:		Line 70:
	* ETSI MEC offers a plethora of open APIs to enable communication with edge and network resources. The one which is more related to interact with our 5G network implementation is the MEC012 RNIS API.		* ETSI MEC offers a plethora of open APIs to enable communication with edge and network resources. The one which is more related to interact with our 5G network implementation is the MEC012 RNIS API.

	* Specifically, for the RAN part we ~~will be~~ working with a commercial solution such as the Amarisoft gNB, in which the connected users ~~will~~ access the MEC applications. Therefore, an integration between Amarisoft RAN and RNIS is needed.		* Specifically, for the RAN part we are working with a commercial solution such as the Amarisoft gNB, in which the connected users access the MEC applications. Therefore, an integration between Amarisoft RAN and RNIS is needed.

	* More specifically, the interaction between the user equipment or the 5G network administrators with Amarisoft is done through a proprietary WebSocket API. Thus, one of our main contributions ~~will be~~ to map WebSocket actions to the RNIS API, in order to extract all the appropriate information for the users and their QoS requirements.		* More specifically, the interaction between the user equipment or the 5G network administrators with Amarisoft is done through a proprietary WebSocket API. Thus, one of our main contributions is to map WebSocket actions to the RNIS API, in order to extract all the appropriate information for the users and their QoS requirements.

	== Deployment of virtual classroom application ==		== Deployment of virtual classroom application ==
Line 81:		Line 81:


	* These components ~~will be~~ dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.		* These components are dockerized and deployed in a cloud-native fashion, in which the virtual classroom server runs over the Kubernetes infrastructure as a pod to conduct our experiment.