I want to make testbed for testing the my own algorithm in mininet. I want to setup link data traffic rate, control traffic rate and link processing rate. but i am not able to it. if anyone have idea how to set up all these. please help me.
thanks,
abha
TL;DR Use D-ITG to generate traffic of your choice.
To define a topology in Mininet -
You can use the MininetEdit.py application in mininet/examples/miniedit.py folder. This will create a .py file defining the topology. You could have also written the same code to create the topology, the MininetEdit application is just a GUI to make it easy.
A sample topology definition looks something like this -
(I have created a simple network with 2 hosts h1, h2 connected to a switch s1)
#!/usr/bin/python
from mininet.net import Mininet
... #More import calls
def myNetwork(net):
info( '*** Add switches\n')
s1 = net.addSwitch('s1')
info( '*** Add hosts\n')
h1 = net.addHost('h1',ip='10.0.0.1',defaultRoute=None)
h2 = net.addHost('h2',ip='10.0.0.2',defaultRoute=None)
info( '*** Add links\n')
net.addLink(h1, s1,bw=200,delay='0ms',loss=0,max_queue_size=1000)
net.addLink(h2, s1,bw=200,delay='0ms',loss=0,max_queue_size=1000)
return net
You can set the maximum link rate / bandwitdh in the MininetEdit app, or change the bw parameter in the addLink function in the code file manually.
If you want to generate some real traffic on this mininet topology, use D-ITG. This is a simple tool that will allow you to generate traffic with different distributions, inter-arrival times, packet sizes, etc.,
So if you want to generate constant rate traffic of say rate KB/s from host h1 to h2, you can follow these steps -
Run xterm h1 from mininet instance
Run the following command on the terminal of h1
ITGSend -a <ip_of_h2> -T UDP -C <rate> -c <packet_size>
You can refer the D-ITG manual for more.
Related
We have a processor, lets say in "NiFi Flow">>"A">>"B">>"C" and we want to provide its output (lets say using an Output Port) to a (let say using an Input Port) which is in "NiFi Flow">>"1">>"2">>"3".
How can we connect those Input/Output ports on different levels / scopes on the same installation using the GUI?
I have a celery app which has to be pinged by another app. This other app uses json to serialize celery task parameters, but my app has a custom serialization protocol. When the other app tries to ping my app (app.control.ping), it throws the following error:
"Celery ping failed: Refusing to deserialize untrusted content of type application/x-stjson (application/x-stjson)"
My whole codebase relies on this custom encoding, so I was wondering if there is a way to configure a json serialization but only for this ping, and to continue using the custom encoding for the other tasks.
These are the relevant celery settings:
accept_content = [CUSTOM_CELERY_SERIALIZATION, "json"]
result_accept_content = [CUSTOM_CELERY_SERIALIZATION, "json"]
result_serializer = CUSTOM_CELERY_SERIALIZATION
task_serializer = CUSTOM_CELERY_SERIALIZATION
event_serializer = CUSTOM_CELERY_SERIALIZATION
Changing any of the last 3 to [CUSTOM_CELERY_SERIALIZATION, "json"] causes the app to crash, so that's not an option.
Specs: celery=5.1.2
python: 3.8
OS: Linux docker container
Any help would be much appreciated.
Changing any of the last 3 to [CUSTOM_CELERY_SERIALIZATION, "json"] causes the app to crash, so that's not an option.
Because result_serializer, task_serializer, and event_serializer doesn't accept list but just a single str value, unlike e.g. accept_content
The list for e.g. accept_content is possible because if there are 2 items, we can check if the type of an incoming request is one of the 2 items. It isn't possible for e.g. result_serializer because if there were 2 items, then what should be chosen for the result of task-A? (thus the need for a single value)
This means that if you set result_serializer = 'json', this will have a global effect where all the results of all tasks (the returned value of the tasks which can be retrieved by calling e.g. response.get()) would be serialized/deserialized using the json-serializer. Thus, it might work for the ping but it might not for the tasks that can't be directly serialized/deserialized to/from JSON which really needs the custom stjson-serializer.
Currently with Celery==5.1.2, it seems that task-specific setting of result_serializer isn't possible, thus we can't set a single task to be encoded in 'json' and not 'stjson' without setting it globally for all, I assume the same case applies to ping.
Open request to add result_serializer option for tasks
A short discussion in another question
Not the best solution but a workaround is that instead of fixing it in your app's side, you may opt to just add support to serialize/deserialize the contents of type 'application/x-stjson' in the other app.
other_app/celery.py
import ast
from celery import Celery
from kombu.serialization import register
# This is just a possible implementation. Replace with the actual serializer/deserializer for stjson in your app.
def stjson_encoder(obj):
return str(obj)
def stjson_decoder(obj):
obj = ast.literal_eval(obj)
return obj
register(
'stjson',
stjson_encoder,
stjson_decoder,
content_type='application/x-stjson',
content_encoding='utf-8',
)
app = Celery('other_app')
app.conf.update(
accept_content=['json', 'stjson'],
)
You app remains to accept and respond stjson format, but now the other app is configured to be able to parse such format.
I think my choice of words is correct. I want to take a group of switches and compile a list of Ip addresses and specific interfaces to have netmiko push commands to. For instance, scan all cisco switches and put together a list of all interfaces in vlan X and not being used. Can someone point me in the right direction of how to do this?
Sounds like you need to figure out the different steps to work out your solution.
Maybe something this:
Connect to switch
run show commands
config interface to vlan xx
I don't see any code or anything you have attempted so far but here is a simple flow for looping through a list of IP addresses.
#Python 3.7
from netmiko import ConnectionHandler
username = "user"
password = "password"
for ip in IPlist:
# netmiko code profiles;
cisco ={
"host":IP,
"username":username,
"password":password,
"device_type: "cisco_ios"
}
with ConnectHandler(**cisco) as ssh_conn:
print(sshcon.find_prompt())
# do stuff here.
After implementing the answer to this question on how to set up a script for time visualization in this project (which uses a small extension to the published Log::Timeline that allows me to set the logging file from the program itself), I still get the same error
12:18 Timeline connection error: Could not get timeline data: java.net.ConnectException: Conexión rehusada
(which means refused connection). I've also checked the created files, and they are empty, they don't receive anything. I'm using this to log:
class Events does Log::Timeline::Event['ConcurrentEA', 'App', 'Log'] { }
(as per the README.md file). It's probably the case that there's no such thing as a default implementation, as shown in the tests, but, in that case, what would be the correct way of making it print to the file and also connect to the timeline visualizer?
If you want to use the timeline visualization, leave the defaults for logging, commenting out any modification of the standard logging output. In my case:
#BEGIN {
# PROCESS::<$LOG-TIMELINE-OUTPUT>
# = Log::Timeline::Output::JSONLines.new(
# path => log-file
# )
#}
Not really sure if this would have happened if an output file is defined using an environment variable, but in any case, better to be on the safe side. You can use the output file when you eventually drop the script into production.
I'm working on integrating an experiment in psychopy with the eyelink eyetracking system. The way to do this seems to be through pylink. Unfortunately I'm really unfamiliar with pylink and I was hoping there was a sample of an experiment that combines the two. I haven't been able to find one. If anyone would be able to share an example or point me towards a more accessible manual than the pylink api that sr-research provides I'd be really grateful.
Thanks!
I am glad you found your solution. I have not used iohub, but we do use psychopy and an eyelink and therefore some of the following code may be of use to others who wish to invoke more direct communication. Note that our computers use Archlinux. If none of the following makes any sense to you, don't worry about it, but maybe it will help others who are stumbling along the same path we are.
Communication between experimental machine and eye tracker machine
First, you have to establish communication with the eyelink. If your experimental machine is turned on and plugged into a live Eyelink computer then on linux you have to first set your ethernet card up, and then set the default address that Eyelink uses (this also works for the Eyelink 1000 - they kept the same address). Note your ethernet will probably have a different name than enp4s0. Try simply with ip link and look for something similar. NB: these commands are being typed into a terminal.
#To set up connection with Eyelink II computer:
#ip link set enp4s0 up
#ip addr add 100.1.1.2/24 dev enp4s0
Eyetracker functions
We have found it convenient to write some functions for talking to the Eyelink computer. For example:
Initialize Eyetracker
sp refers to the tuple of screenx, screeny sizes.
def eyeTrkInit (sp):
el = pl.EyeLink()
el.sendCommand("screen_pixel_coords = 0 0 %d %d" %sp)
el.sendMessage("DISPLAY_COORDS 0 0 %d %d" %sp)
el.sendCommand("select_parser_configuration 0")
el.sendCommand("scene_camera_gazemap = NO")
el.sendCommand("pupil_size_diameter = %s"%("YES"))
return(el)
NB: the pl function comes from import pylink as pl. Also, note that there is another python library called pylink that you can find on line. It is probably not the one you want. Go through the Eyelink forum and get pylink from there. It is old, but it still works.
Calibrate Eyetracker
el is the name of the eyetracker object initialized above. sp screen size, and cd is color depth, e.g. 32.
def eyeTrkCalib (el,sp,cd):
pl.openGraphics(sp,cd)
pl.setCalibrationColors((255,255,255),(0,0,0))
pl.setTargetSize(int(sp[0]/70), int(sp[1]/300))
pl.setCalibrationSounds("","","")
pl.setDriftCorrectSounds("","off","off")
el.doTrackerSetup()
pl.closeGraphics()
#el.setOfflineMode()
Open datafile
You can talk to the eye tracker and do things like opening a file
def eyeTrkOpenEDF (dfn,el):
el.openDataFile(dfn + '.EDF')
Drift correction
Or drift correct
def driftCor(el,sp,cd):
blockLabel=psychopy.visual.TextStim(expWin,text="Press the space bar to begin drift correction",pos=[0,0], color="white", bold=True,alignHoriz="center",height=0.5)
notdone=True
while notdone:
blockLabel.draw()
expWin.flip()
if keyState[key.SPACE] == True:
eyeTrkCalib(el,sp,cd)
expWin.winHandle.activate()
keyState[key.SPACE] = False
notdone=False
Sending and getting messages.
There are a number of built-in variables you can set, or you can add your own. Here is an example of sending a message from your python program to the eyelink
eyelink.sendMessage("TRIALID "+str(trialnum))
eyelink.startRecording(1,1,1,1)
eyelink.sendMessage("FIX1")
tFix1On=expClock.getTime()
Gaze contingent programming
Here is a portion of some code that uses the eyelink's most recent sample in the logic of the experimental program.
while notdone:
if recalib==True:
dict['recalib']=True
eyelink.sendMessage("RECALIB END")
eyelink.startRecording(1,1,1,1)
recalib=False
eventType=eyelink.getNextData()
if eventType==pl.STARTFIX or eventType==pl.FIXUPDATE or eventType==pl.ENDFIX:
sample=eyelink.getNewestSample()
if sample != None:
if sample.isRightSample():
gazePos = sample.getRightEye().getGaze()
if sample.isLeftSample():
gazePos = sample.getLeftEye().getGaze()
gazePosCorFix = [gazePos[0]-scrx/2,-(gazePos[1]-scry/2)]
posPix = posToPix(fixation)
eucDistFix = sqrt((gazePosCorFix[0]-posPix[0])**2+(gazePosCorFix[1]-posPix[1])**2)
if eucDistFix < tolFix:
core.wait(timeFix1)
notdone=False
eyelink.resetData()
break
Happy Hacking.
rather than PyLink, you might want to look into using the ioHub system within PsychoPy. This is a more general-purpose eye tracking system that also allows for saving data in a common format (integrated with PsychoPy events), and provides tools for data analysis and visualisation.
ioHUb is built to be agnostic to the particular eye tracker you are using. You just need to create a configuration file specific to your EyeLink system, and thereafter use the generic functions ioHiv provides for calibration, accessing gaze data in real-time, and so on.
There are some teaching resources accessible here: http://www.psychopy.org/resources/ECEM_Python_materials.zip
For future readers, I wanted to share my library for combining pylink and psychopy. I've recently updated it to work with python 3. It provides simple to use, high level functions.
https://github.com/colinquirk/templateexperiments/tree/master/eyelinker
You could also work at a lower level with the PsychoPyCustomDisplay class (see the pylink docs for more info about EyeLinkCustomDisplay).
For an example of it in use, see:
https://github.com/colinquirk/ChangeDetectionEyeTracking
(At the time of writing, this experiment code is not yet python 3 ready, but it should still be a useful example.)
The repo also includes other modules for creating experiments and recording EEG data, but they are not necessary if you are just interested in the eyelinker code.