Various parts of the code are factored into different modules based on functionality.
utils.py: Utility functions for printing messages, detecting a stop signal (Ctrl+C), and reading and writing checkpoints.genseq.py: Functions for generating synthetic sequences of image offloading metrics and inter-arrival times using different temporal models. Note that the offloading metric sequences are represented with image indices from the raw dataset to save memory.data.py: Code to load the data from the rawnpzfile, and a tuple sampler that randomly samples tuples of offloading metrics and inter-arrival times (for the current and the next state) and reward.bucket.py: Function to handle various bucket operations:- Convert rate and depth to integers.
- Various utilities to interpret a long vector output from a neural network as a table of (n, a) values. Do operations to correctly do the whole
max_a' Q(n'=(n,a), a'). - Code for computing an i.i.d. mdp policy.
model.py: Functions to create different kinds of Keras models, including one from an iid policy threshold vector.bstream.py: Generates tensorflow compiled @tf.function's to sequentially simulate offloading decision making with a model and token bucket.