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https://github.com/cerob/slicesim

5G Network Slicing Simulation
https://github.com/cerob/slicesim

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5G Network Slicing Simulation

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# SliceSim: A Simulation Suite for Network Slicing in 5G Networks

**Abdurrahman Dilmaç** (abdurrahman.d _at_ icloud _dot_ com)  

**Muhammed Emin Güre** (memingure _at_ gmail _dot_ com)  

*Project Advisor:* **Prof. Tuna Tuğcu**

***

### Introduction

5G widely defines network slicing concept which aims to provide different and separate dedicated logical networks that can be customized to respective services. All slices under a cloud infrastructure are put together with their different requirements, e.g. bandwidth, latency



The purpose of this project is to provide a simulation suite for a network consisting of base stations and clients that possible scenarios of 5G can fit into and make analysis of different concepts easier.



### Approach

- Discrete event simulation

- Using **Python 3.7, Simpy, Matplotlib, KDTree**

- **YAML** for reading input configurations

- Asynchronous programming

- Definitions:

	- **Clients:** Simulation consumers. Generates consume requests by given distribution parameters.

	- **Slices of Base Stations:** Simulation resources.



### Input



#### Settings

```yaml

settings:

  simulation_time: 100 # in seconds

  num_clients: 100

  limit_closest_base_stations: 5 # how many base stations stored in a client instance

  statistics_params:

    warmup_ratio: 0.05 # statistic collection will start from this point

    cooldown_ratio: 0.05 # statistic collection will end at this point

    # Statistic collection will be in this area of the coordinate system

    x:

      min: 0

      max: 1980

    y:

      min: 0

      max: 1980

  logging: False # saving logs to a file

  log_file: output.txt # name of the log file

  plotting_params:

    plotting: True # plot the statistics after execution

    plot_save: True # save plot as image

    plot_show: False # show plot after execution

    plot_file: output.png # name of the plot image

    plot_file_dpi: 1000 # dots per inch for plot image

    scatter_size: 15

```



#### Slices

```yaml

slices:

  slice_name: # name of the slice

    delay_tolerance: 10

    qos_class: 5

    bandwidth_guaranteed: 0 # in bps

    bandwidth_max: 100000000 # in bps

    client_weight: 0.39 # [0,1] - ratio of the clients subscribed to this slice in the system. All weights for slices must be 1 in total

    threshold: 0 # for dynamic slicing (future work)

    # defines the bit usage pattern for client subscribed to this slice

    usage_pattern:

      distribution: randint # distribution name

      params: # distribution parameters

        - 4000000 # min value for this example

        - 800000000 # max value for this example

  slice_name_2:

  	...

```



#### Base Stations

```yaml

base_stations:

  - x: 182 # in meters

    y: 1414 # in meters

    capacity_bandwidth: 20000000000 # in bps

    coverage: 224 # in meters

    # ratios of the slices in this specific base station

    # must be 1 in total for each base station

    ratios:

      slice_name: 0.20 # [0,1]

      slice_name_2: 0.59 # [0,1]

      ...

  - ...

```



#### Mobility Patterns

```yaml

mobility_patterns:

  mobility_pattern_1:

    distribution: normal # distribution name

    params: # distribution parameters

      - 0 # mean value for this example

      - 7 # standard deviation name for this example

    client_weight: 0.10 # [0,1] - ratio of the clients assigned to this pattern in the system. All weights must be 1 in total

  mobility_pattern_2:

  	...

```



#### Client population

```yaml

clients:

  location: # populates the area with the given distributions

    x:

      distribution: randint

      params:

        - 0

        - 1980

    y:

      distribution: randint

      params:

        - 0

        - 1980

  usage_frequency: # defines the usage generation intervals of clients

    distribution: randint

    params:

      - 0

      - 100000

    divide_scale: 1000000 # scaling factor

```



### Usage

Python 3 is required with required dependencies listed in `requirements.txt` installed. Please do not send us email if you haven't done this.



```bash

python -m slicesim 

```



### Example Output

![Example output for 5000 client in 3600s](https://github.com/cerob/slicesim/blob/master/examples/output_n5000_t3600.png)



### Conclusion

- Increasing number of clients increases used bandwidth,

and yet the simulation showed that block ratio also

elevates for this specific configurations.



This simulation tool can be used for such scenarios as well:



- Testing the effect of different dynamic slicing algorithms

on block and handover ratios.

- Analyzing various mobility patterns of clients using

different statistical distributions.

- Observing the effect of usage frequency of clients and the

effect of clients those are distributed unequally.

- Various Proof of Concepts like common base stations for

multiple service providers.



### Future Work

- Customizable shapes for base station coverages

- Improvements of the software performance

- Dynamic slicing mechanism

- Generation of more test configurations

- Video output of a running simulation



### References

1. 5GPPP Architecture Working Group. View on 5G Architecture. Version 2.0, December 2017

2. CellMapper - https://www.cellmapper.net (10.05.2019)

3. FatihMunicipalityGeographicInformationSystem-

https://gis.fatih.bel.tr/webgis (13.05.2019)

4. https://venturebeat.com/2018/12/12/decoding-5g-a-cheat-

sheet-for-next-gen-cellular-concepts-and-jargon/ (17.03.2019)