Slicenet

A mininet-like Simulator for simulating experimentations around Network Slicing

What is `slicenet` ?

A mininet like Simulator for simulating large macro network topologies along with UEs, Application nodes, Access networks, Transport networks, Core network & Data network.
Network Slicing policies & optimization logics can be experimented in various topologies and performance of each experiment can be measured in predictable & reproducible manner.
Unlike mininet, Slicenet only “simulates” the network. Hence without any real compute & power resources, lot of resource optimization, scheduling, prioritization & capacity models can be experimented in consistent way.
By abstracting topology out of the traffic pattern, same topology can be experiments with different traffic pattern. Mobility scenarios can also be easily experimented

Why `slicenet` ?

Operators & Researchers have a simple & efficient way to try their experiments without the need to emulate entire physical / virtual topology
Borrows the topology, simulation & experiments concepts on all the previous tools and provides a consistent way to experiment & report the findings
Based on python and thereby extending itself to popular ML frameworks like TensorFlow / PyTorch to native use ML/DL/NN models as part of the experiments
Since Slicenet is just simulation, it can be run in Jupyter notebook setup as well (unlike mininet which is based on linux namespaces). This makes
Slicenet extremely useful for researchers to quickly export the findings of the experiments and generates charts / visualizations and share it with wider research community

What can you simulate with `slicenet` ?

Slicenet can be used to simulate experimetions around optimizations for

Slice Orchestration
Slice Admission
Slice SLA Adherence & Violation Avoidance
Slice Composition
Dynamic Slicing Negotiation techniques etc

Every experiment can be conducted in various topologies and performance can be measured in consistent fashion.

If you find Slicenet to be useful in your research work, please cite the following publication:

@INPROCEEDINGS{10323887,
  author={KumarSkandPriya, Viswanath and Dandoush, Abdulhalim and Diaz, Gladys},
  booktitle={2023 International Symposium on Networks, Computers and Communications (ISNCC)}, 
  title={Slicenet: a Simple and Scalable Flow-Level Simulator for Network Slice Provisioning and Management}, 
  year={2023},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ISNCC58260.2023.10323887}}

Other preprints

TechRxiv

[1] V. Kumar Skand Priya, A. Dandoushand . gladys . diaz, “Slicenet: A Simple and Scalable Flow-Level Simulator for Network Slice Provisioning and Management”. TechRxiv, 18-Oct-2023, doi: 10.36227/techrxiv.24311254.v1.

@article{KumarSkandPriya2023,
author = "Viswanath Kumar Skand Priya and Abdulhalim Dandoush and gladys diaz",
title = "{Slicenet: A Simple and Scalable Flow-Level Simulator for Network Slice Provisioning and Management}",
year = "2023",
month = "10",
url = "https://www.techrxiv.org/articles/preprint/Slicenet_A_Simple_and_Scalable_Flow-Level_Simulator_for_Network_Slice_Provisioning_and_Management/24311254",
doi = "10.36227/techrxiv.24311254.v1"

arXiv

@misc{kumarskandpriya2023slicenet,
      title={Slicenet: a Simple and Scalable Flow-Level Simulator for Network Slice Provisioning and Management}, 
      author={Viswanath KumarSkandPriya and Abdulhalim Dandoush and Gladys Diaz},
      year={2023},
      eprint={2310.11033},
      archivePrefix={arXiv},
      primaryClass={cs.NI}
}

Installation (available as a pip package)

pip install slicenet

Example Usage

Using Slicenet Simulator to simulate 2 Slices and visualize the Slice Composition and Slice Utilization ratios of NFs

1. Import Slicenet

from slicenet.entities.cloud import Cloud
from slicenet.entities.nf import Nf
from slicenet.mgrs.nfMgr import NfMgr
from slicenet.mgrs.sliceMgr import SliceMgr
from slicenet.entities.staticslice import StaticSlice
from slicenet.entities.service import Service

from tabulate import tabulate
import random
import logging
logger = logging.getLogger('example')

logging.basicConfig(format='%(asctime)s %(name)s %(module)s %(levelname)s: %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.INFO)

2. Create Simulation Topology (Clouds & NFs)

clouds = []
nfs = []

# Create Cloud Objects
clouds.append(Cloud(1000, 10, 10000, name="edge cloud"))
clouds.append(Cloud(2000, 20, 20000, name="wan cloud"))

# Create NF Objects
nf1 = Nf('NF 1', 100,9,1234)
nf2 = Nf('NF 2', 100,9,1234)
nf3 = Nf('NF 3', 200,1,1234)
nf4 = Nf('NF 4', 200,1,1234)

nfs.append(nf1)
nfs.append(nf2)
nfs.append(nf3)
nfs.append(nf4)

# Set NF Schedular policy - This will help NfMgr to schedule NFs into the cloud
# First available method chooses the first available cloud according to NF requirements.
NfMgr.setSchedularPolicy('first-available-method')

# Register the clouds with NfMgr
for c in clouds:
    NfMgr.registerCloud(c)

# Deploy NFs into the cloud
for n in nfs:
    NfMgr.deployNf(n)

3. Add Slices to the infrastructure

Create 3 Slices (Vedio Streaming , Emergency Slice & Security Slice)

# Create slice objects by specifying the slice composition & % resource allocation
# For eg, VS slice will take 20% of Nf 1 resource. 20% of Nf 2 resource etc.
vs_slice = StaticSlice("Vedio Streaming Slice")
vs_slice.composeSlice(nf1.id, 20)
vs_slice.composeSlice(nf2.id, 20)
vs_slice.composeSlice(nf3.id, 20)
vs_slice.composeSlice(nf4.id, 20)

es_slice = StaticSlice("Emergency Slice")
es_slice.composeSlice(nf1.id, 50)
es_slice.composeSlice(nf2.id, 34)
es_slice.composeSlice(nf3.id, 60)
es_slice.composeSlice(nf4.id, 12)

ss_slice = StaticSlice("Security Slice")
ss_slice.composeSlice(nf1.id, 10)
ss_slice.composeSlice(nf2.id, 10)
ss_slice.composeSlice(nf3.id, 10)
ss_slice.composeSlice(nf4.id, 10)

# Use Slice Manager to deploy the slices
SliceMgr.deploySlice(vs_slice)
SliceMgr.deploySlice(es_slice)
SliceMgr.deploySlice(ss_slice)

#Dump Slice information
SliceMgr.dumpSlices()

┌──────────────────────────────────────┬───────────────────────┬─────────────────────────┐
│ Slice ID                             │ Slice Name            │   Slice Availablity (%) │
├──────────────────────────────────────┼───────────────────────┼─────────────────────────┤
│ 38b055c5-440b-4f32-b287-05785b2a73ae │ Vedio Streaming Slice │                     100 │
├──────────────────────────────────────┼───────────────────────┼─────────────────────────┤
│ 92aa951d-40c4-4c4c-a2ce-a8d76a22482e │ Emergency Slice       │                     100 │
├──────────────────────────────────────┼───────────────────────┼─────────────────────────┤
│ ad5c13f6-166c-4863-a8cf-b14968d7c79f │ Security Slice        │                     100 │
└──────────────────────────────────────┴───────────────────────┴─────────────────────────┘

4. Now dump the slice utilization details for each NF

NfMgr.dumpNfInfoSliceDetails()

┌──────────────┬──────────────────────────────────────┬───────────┬───────────────────────────────┐
│ Cloud Name   │ NF ID                                │ NF Name   │   Overall Utilization Ratio % │
├──────────────┼──────────────────────────────────────┼───────────┼───────────────────────────────┤
│ edge cloud   │ d64dba50-9a87-4837-9047-db3c873b4cb8 │ NF 1      │                            80 │
├──────────────┼──────────────────────────────────────┼───────────┼───────────────────────────────┤
│ wan cloud    │ 5cc70826-67e8-4e92-83e1-28863abcfafe │ NF 2      │                            64 │
├──────────────┼──────────────────────────────────────┼───────────┼───────────────────────────────┤
│ edge cloud   │ 8e34d222-188a-4c7a-9b12-46d0209fb081 │ NF 3      │                            90 │
├──────────────┼──────────────────────────────────────┼───────────┼───────────────────────────────┤
│ wan cloud    │ 9f7da27d-0e69-4e69-b589-3071f84fe88c │ NF 4      │                            42 │
└──────────────┴──────────────────────────────────────┴───────────┴───────────────────────────────┘

5. Also visualize the data

Visualize the cloud utilization ratio of all clouds

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.legend import Legend

import random

cloudUtilDicts = []
legends = []

for c in clouds:
  cloudUtilDicts.append(NfMgr.getCloudUtilRatio(c.id))

x = np.array(['CPU', 'RAM', 'HDD'])

fig, ax = plt.subplots(figsize = (10, 5))
plt.title('Cloud Utilization Ratio')

i=0
col = ['r', 'g']

def getRandColor():
  rgb = [random.uniform(0, 1),
         random.uniform(0, 1),
         random.uniform(0, 1),]
  return rgb

for d in cloudUtilDicts:
  y = np.array([d['cpu'], d['ram'], d['hdd']])
  ax_ = ax.twiny()
  col = getRandColor()
  c = clouds[cloudUtilDicts.index(d)]
  print (c.name)
  legends.append(c.name)
  ax_.bar(x, y, color = col, label = c.name)

# giving labels to the axises
ax.set_xlabel('Cloud Resources', color = 'g')
#ax.legend(legends)
#ax.set_ylabel('Cloud 1 Util Ratio', color = 'g')

# secondary y-axis label
#ax2.set_ylabel('Cloud 2 Util Ratio', color = 'b')

# defining display layout
#plt.tight_layout()

# show legend
plt.legend(legends)

# show plot
plt.show()

NfMgr.dumpCloudInfo()

edge cloud
wan cloud

┌──────────────────────────────────────┬────────────┬──────────┬──────────┬──────────┐
│ Cloud ID                             │ Name       │   RAM(%) │   CPU(%) │   HDD(%) │
├──────────────────────────────────────┼────────────┼──────────┼──────────┼──────────┤
│ 033d378c-68f7-453d-8ff3-4726b0a5e0b5 │ edge cloud │       30 │      100 │    24.68 │
├──────────────────────────────────────┼────────────┼──────────┼──────────┼──────────┤
│ 31fc96e4-aa18-4914-bbe9-786543b345d6 │ wan cloud  │       15 │       50 │    12.34 │
└──────────────────────────────────────┴────────────┴──────────┴──────────┴──────────┘

Visualize Slice Utilization ratio of each NF

import matplotlib.pyplot as plt

#labels = ["Video Streaming Slice", "Emergency Slice", "Unused"]

'''
fig = plt.figure(figsize=(10, 6))
'''
fig = plt.figure(figsize=(6,6))
sep = 0

for n in nfs:
  weights = []
  labels = []
  for k,v in n.slices.items():
    weights.append(v)
    labels.append(SliceMgr.slices[k].name)
  weights.append(n.getRemainingCapacity())
  labels.append("Unused")
  ax_ = fig.add_axes([0+sep, 0, .5, .5], aspect=1)
  '''
  ax_.pie(weights, labels=labels, radius = 1, autopct='%1.1f%%')
  '''
  ax_.pie(weights, radius = 1, autopct='%1.1f%%')
  ax_.set_title(n.name)
  sep += 0.4

plt.legend(labels, bbox_to_anchor=(1.05, 1.0))
plt.show()

Cloud Resources to Slice Mapping

import matplotlib.pyplot as plt
'''
fig = plt.figure(figsize=(25, 10))
'''
fig = plt.figure(figsize=(8, 8))
sep = 0

for c in clouds:
  weights = []
  labels = []
  nfs = c.deployedNfs
  for n in nfs:
    for k,v in NfMgr.nfs[n].slices.items():
      nfname = NfMgr.nfs[n].name
      slicename = SliceMgr.slices[k].name
      weights.append(v)
      labels.append(f"{nfname}# {slicename}")
  weights.append((100 - c.returnAvgCloudUtilRatio())/100)
  #weights.append(0)
  labels.append('Unused')
  ax_ = fig.add_axes([0+sep, 0, .5, .5], aspect=1)
  '''
  ax_.pie(weights, labels=labels, radius = 1, autopct='%1.1f%%')
  '''
  ax_.pie(weights, radius = 1, autopct='%1.1f%%')
  ax_.set_title(c.name)
  sep += 0.5

plt.legend(labels, bbox_to_anchor=(1.05, 1.0))

'''
plt.legend(bbox_to_anchor=(1,0),
           bbox_transform=plt.gcf().transFigure,
           title="Slice Distribution in Cloud")
'''
plt.show()

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
E2E_Slicenet_example_files		E2E_Slicenet_example_files
.gitignore		.gitignore
E2E_Slicenet_example.ipynb		E2E_Slicenet_example.ipynb
LICENSE		LICENSE
README.md		README.md
_config.yml		_config.yml
slicenet-1-logo.png		slicenet-1-logo.png
slicenet-1-tr-logo.png		slicenet-1-tr-logo.png

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kspviswa/slicenet-pages

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Slicenet

A mininet-like Simulator for simulating experimentations around Network Slicing

What is slicenet ?

Why slicenet ?

What can you simulate with slicenet ?