.. or Mobile Agents with Python
What are mobile agents?
"A mobile agent is a process that can transport its state from one environment to another, with its data intact, and be capable of performing appropriately in the new environment", source: Wikipedia.
Why Mobile Agents .. or how to deal with Data Gravity
The phrase "with its data" most likely refers to the agent's state data (and not all types of data), since one of the nicest properties of mobile agents is that they can move to where potentially huge amounts of data is. By plotting the curve "data gravity" - i.e. a rough estimate of how long time it takes to empty/fill/process a hard drive over a network connection - hard disk size divided by (typical) ethernet network speed over the last 20 years - the motivation for moving code to data (and not vice versa) is clearly increasing, making mobile agents a potentially interesting approach.
i) receive and run binary code
python example: receive python compiled to binary with Shedskin and g++
ii) receive and compile source code and run it
python example: receive c source code and compile/integrate it with Python using Cinpy, or use Shedskin/g++ on received Python code
iii) receive and run interpreter on source code
python example: receive python code and interpret using the eval() method.
Bandwidth
If the mobile agents move around a bit, you probably want their representation as compact as possible to reduce bandwidth requirements, i.e. prefer agents represented in (small amounts of) source code - alternative ii) or iii) - over (larger amounts of) binary code - alternative i).
Processing
compiled code (or just-in-time compiled code) is usually more efficient than interpreted code (interpreted code can perhaps be seen as analog to the "gas guzzling cars" of computing wrt resource utilization, fortunately there are tools to deal with that), so alternative ii) is probably preferred over iii), and with the cinpy case compilation overhead is negligible (a few milliseconds to compile and make a short C method ready to be called from Python), which matter if you have a large amount of distributed mobile agents. Pareto principle also matters for mobile agents, so a mix (80-99% of code) in interpreted Python and the things that really need to perform in C (quickly compiled with cinpy) might be a common mix.
Example of Cinpy-wrapped C function in python
fibc=cinpy.defc(
"fib",
ctypes.CFUNCTYPE(ctypes.c_int,ctypes.c_int),
"""
int fib(int x) {
if (x<=1) return 1; return fib(x-1)+fib(x-2); } """)
Security
In case mobile agents move around on the cloud it is nice to know that the agent you receive is from a known source (yourself), this can e.g. be done using ezPyCrypto's signString() to sign the agent source and then use verifyString() methods on the signed agent source code together with its signature to check the origin of the agent (assuming the signer's public key is available on the receiving end).
disclaimer: this posting (and all others on this blog) only represents my personal views.
