Upon further examination, the issue of the VM freezing due to an excessive quantity of processes running while executing a script raises questions. It is unclear whether this issue stems from shortcomings in the multiprocessing approach or limitations of the VM itself based on current information.

A potential solution could involve conducting a scaling experiment by varying the number of processes involved in sending data. Testing different configurations, such as distributing the workload among varying numbers of processes, may provide insights into performance optimization.

To analyze any potential bottlenecks resulting from different parallelism choices, tools like xperf for Windows or oprofile for Linux can be utilized. By monitoring factors like CPU cache usage and memory allocation, it becomes possible to identify optimal settings for improved performance.

In line with previous experiences, it is generally recommended to match the number of multiprocessing processes with available CPU cores for optimal efficiency. However, exceptions may exist if certain operations benefit from a higher number of processes for better resource utilization. Profiling and additional scaling experiments are necessary to determine the most effective approach.

While Python is typically single-threaded, it's important to note that handling tasks such as sending network packets falls under IO-bound operations, making it a suitable candidate for multi-threading. Your main thread can remain unoccupied while the packets are being transmitted, especially if you write your code with an asynchronous approach in mind.

For more information on implementing async TCP networking in Python, check out the official documentation at https://docs.python.org/3/library/asyncio-protocol.html#tcp-echo-client.

When dealing with a bottleneck that is http based (referred to as "sending packets"), the Global Interpreter Lock (GIL) should not pose a major problem.

However, if there are computations taking place within Python, then the GIL could potentially obstruct progress. In such cases, process-based parallelism would be the recommended approach.

A common misconception is thinking that each task requires its own process. Fortunately, utilizing Python's Pool class allows for the creation of a group of workers that can receive tasks from a queue efficiently.

import multiprocessing


def send_pkts(ip):
   ...


number_of_workers = 8

with multiprocessing.Pool(number_of_workers) as pool:
    pool.map(send_pkts, list[:1500])

By implementing this setup, you will have a total of number_of_workers + 1 processes running simultaneously (the workers along with the original process), all executing the send_pkts function concurrently.

Your quest for optimal performance is hindered by a specific bottleneck: the send_pkts() method. This method not only sends packets but also crafts them:

def send_pkts(ip):
#craft packet
while True:
    #send packet
    time.sleep(randint(0,3))

Sending packets is mainly an I/O bound task, while crafting packets leans towards being a CPU bound task. To address this issue effectively, it's essential to separate these tasks into two distinct processes:

1. Crafting a packet
2. Sending a packet

To assist in resolving this issue, I've developed a basic socket server and client application that work together to craft and transmit packets efficiently. The concept involves segregating the packet crafting process into a standalone entity that generates packets placed in a shared queue. Concurrently, a pool of threads pulls these packets from the queue and dispatches them to the server. Furthermore, the responding data from the server is stored in another common queue, primarily utilized for testing purposes rather than core functionality. These threads terminate when they encounter a None signal (commonly referred to as a poison pill) within the queue.

The `server.py` script showcases the setup for the socket server, while `client.py` demonstrates the configuration for clients sending packets:

`server.py`:

[python code here]

`client.py`:

[python code here]

A bash script `run.sh` automates the execution of the server and client scripts with varying parameters to assess performance under different conditions:

[bash script here]

$ ./run.sh -s=1024 -n=1500 -t=300 -h=localhost -p=9999

1500 packets received in 4.70330023765564 seconds

$ ./run.sh -s=1024 -n=1500 -t=1500 -h=localhost -p=9999

1500 packets received in 1.5025699138641357 seconds

For accurate insights, consider setting the log level in `client.py` to `DEBUG`. Please note the script duration may exceed the time reported due to finalization overhead when using multiple threads. Despite this, thread processing concludes around the 4.7-second mark according to logs.

It’s vital to interpret performance outcomes cautiously based on system specifications. For reference, my system comprises:

- 2 Xeon X5550 @2.67GHz - 24MB DDR3 @1333MHz - Debian 10 - Python 3.7.3

---

An overview of encountered issues during optimization attempts:

1. Single-threaded approach: Expected minimum completion time of 1.5 x num_packets due to delay
2. Multithreading challenges: Probable GIL bottleneck, particularly influenced by packet crafting operations
3. Multiprocessing limitations: Potential constraints on file descriptors due to set limits; adjustments might be necessary
4. Mixed multiprocessing and multithreading concerns: Likely failure due to intensive CPU bound nature of packet crafting

Remember, adhere to the rule of thumb—Threads for I/O bound tasks, Processes for CPU-bound operations.