cn-upb / nfvdeep Goto Github PK

Hello sir, it is my honor to study your project. I can understand the rough process, but I do not know how to use the experiment results and use them to make the figures like that. Could you give me an instruction about the experimental indicators ?(logs, placements in script.py and ray results in tuning.py). I am looking forward to your reply very much.

I'm sorry to bother you, sir. Your project is very exciting, and I have learned a lot through practical experience. But There is a problem now that my script.py file can train normally, but the tune.py file encountered this issue. I tried reinstalling ray, but still displayed this error. Can you help me answer the question?

Hello, thank you for providing a great paper code reproduction! I would like to ask, after I run the script.py, the generated placement.txt shows that the vnf in the sfc is only placed on node 6. I don't know what the problem is. Can you help me answer it?

Hello, thank you for this very wonderful project. This is also what I was looking for for my graduate project.
I have a few questions you can answer these are:
-In the update function in the network.py file, when the SFC exceeds their TTL, will the environment be changed? For example, those SFCs return CPU, Memory, Bandwidth resources to the nodes that the SFCs have embedded into.
-About results: I understand an episode is a set of states from the beginning to the last (from the first SFC that joins the system until the last SFC leaves the system). In the following episodes, the agent is better trained based on the experience gained from the previous episodes. Do I understand that correctly?
Thanks in advance!!!

Frist of all, thank you for the great work you've done! The code of this reproduction is very clear.
Here's my problem.
I add a best_model_save_path parameter to the EvalCallback call in the script.py, so I can get the best model after the training steps. But after I try to evaluate the model using evaluate_policy from stable_baselines3.common.evaluation, I got really confused. The reward I got from this evaluation is negative, which is far away from the episode_reward in the logs, it's even worse than the fisrt eval result during the training. Why is it the case? I looked at the docs of stable-baselines3, the EvalCallback also uses evaluate_policy to get the reward values, so the results should be close.

In my test code, I just load the env like the script.py, and here's my evaluation process.

Agent = getattr(stable_baselines3, args.agent)
model = Agent.load("./testing/evaluation/model/best_model")
print(evaluate_policy(model, env))

Actually, I found this bcs I tried to tune the hyperparameters using Optuna, but the value given by Optuna is negative, while the episode reward is positive and pretty large. I really get confused by this result.
Thanks again!

Expected type 'Union[Env, VecEnv]', got 'StatsWrapper' instead

Hi
I ran the first part of NFVDeep successfully (Experiments/script.py).
But in executing the second part(Hyperparameter Optimization/tune.py) , I encounter the following error:

_File "C:\Users\98913\Documents\Python\NFVdeep-main\tune.py", line 158, in
search_alg=AxSearch(ax_client),
File "C:\Simulate\lib\site-packages\ray\tune\suggest\ax.py", line 173, in init
self._setup_experiment()
File "C:\Simulate\lib\site-packages\ray\tune\suggest\ax.py", line 197, in setup_experiment
raise ValueError(
ValueError: Please specify the mode argument when initializing the AxSearch object or pass it to tune.run().

I spent many hours fixing it but did not get the result.Please help me fix this error.

Hello sir, it is my honor to study your project.
When I run the program, in the [_on_step()] function of EvalLogCallback,
eval_ Envs: List [StatsWrapper]=self. local ["callback"]. eval_ Env. envs
This line of code shows that there is no "callback" variable name . I want to know if the monitor file is missing any content.
I am looking forward to your reply very much.

Thank you for your sharing ,but you forgot to export the package version to the environment.yml.So there is no environment.yml found.

Hello! This is a great program and I download it, but it misses "request.json". Can you share the file with me? My Email address is [email protected]. Thank you very much for your consideration. I am very pleased to hear from you.

Yours sincerely