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https://github.com/6G-Future-Lab-Bavaria/NextGSim


https://github.com/6G-Future-Lab-Bavaria/NextGSim

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README 

        
# NextGSim



NextGSim provides a simulation environment focusing on resource management aspects in mobile edge computing scenarios. It consists of mainly two parts: Radio Network simulation and Edge Computing

simulation. These two simulations are loosely connected to enable both independent and joint use.



# Simulated Architecture



The simulated architecture resembles a joint management server, where edge and radio-related information is simultaneously updated 

to the edge orchestrator and SD-RAN controller. Therefore, novel resource allocation, offloading decisions, and orchestration algorithms can be tested through NextGSim. 





  

  


    Figure 1: Relationship between NextGSim modules.

  



# Radio Simulation

The Radio Access Network (RAN) consists of the user plane and the control plane. 



RAN user plane: The user plane is responsible for creating the scenario (Scenario module), depending on the parameters provided by the user in the 

configuration file. The indoor factory scenario consists of a constant number and placement of gNBs, however, the indoor and outdoor scenarios are reconfigurable.

gNBs placement is characterized by 3D coordinates (X,Y,Z), the coverage defined by the radios of macro or micro gNBs. The Channel model module is responsible for 

generating the signal quality between the serving gNBs and the users roaming in the area covered by the gNBs. 

Meanwhile, the users are placed randomly in the scenario space, characterized by the user's coordinates (x,y,z) and by the velocity v. The velocity value of a user stays constant

during the simulation time after the initialization process. User module is responsible for the generation of the traffic patterns, user's mobility, and user radio resource

control (RRC) states.



RAN control plane: RAN control plane simulates the behaviour of SD-RAN controller.

The module includes the radio resource management algorithms, such as resource allocation via

state-of-the-art algorithms: round-robin, throughput proportional fair, max-CQI.

Further, through the handovers, the module assures the connectivity of the end-users to the 

best serving gNB. The RRC state transitions of the users between RRC Idle, RRC Connected and 

RRC Inactive sets the bases for the sustainability studies for the end-usr devices.



# Edge Computing Simulation

Edge Computing part consists of 3 main modules that are Application, Entities and

Network.



Application: An application is defined by its services which form a directed

acyclic graph(DAG). Microservices can generate, receive and process messages. In such a scenario, a task is completed

when the last microservice in a DAG, processes its message. Different users can share microservices at the edge or have

a dedicated microservice for themselves.



Entities: Entities implemented are Edge Server, Orchestrator, CPU and Router. Edge servers are the computing

nodes that serves users at the edge of the network. They are controlled by the orchestrator which decides where to deploy services,

which services users need to be assigned to, forward radio related information to services and if necessary migrate services

between edge servers. 



CPU module defines the processing behaviour of the services. Different processing behaviour can be implemented

module. As a proof-of-concept example a latency aware processing algorithm is implemented, where messages from users that suffer 

higher radio link latency is prioritized, in order to increase the chance of meeting the deadline of the task.



Router is a simple entity that routes messages between entities like edge servers and base stations. Currently, it only implements

the shortest path algorithm for routing.



Network: This module implements the topology between entities and microservices. Physical entities 

are connected to each other with links that are defined by their bandwidth and latency.



# Examples

Figure 5 in our paper is a result of an experiment to assess the effect of various combinations of radio resource allocation and 

edge application queueing approaches. In order to reproduce the results, run "experiments/experiments_figure5.sh" in your terminal.

To generate the plot out of the results, run "plotting/figure5.py".



Figure 6 in our paper is a result of an experiment to assess the effect of increasing the number of application instances 

and number of users on completion rate of a task. 

In order to reproduce the results, run "experiments/experiments_figure6.sh" in your terminal.

To generate the plot out of the results, run "plotting/figure6.py".



# Publication

nextGSIM: Towards Simulating Network Resource Management for Beyond 5G Networks



If you use this software, please cite it as below.



@inproceedings{jano2023nextgsim,

  title={nextGSIM: Towards Simulating Network Resource Management for Beyond 5G Networks},

  author={Jano, Alba and Mert Bese, Mehmet and Mohan, Nitinder and Kellerer, Wolfgang and Ott, J{\"o}rg},

  booktitle={IEEE Future Networks World Forum},

  year={2023}

}



# Contact Authors



RAN modules - Alba Jano (email: [email protected])



Edge computing modules - Mehmet Mert Bese ([email protected])