Pricing-Driven Resource Allocation in the Computing Continuum – Laboratory Package

This repository contains the implementation used to study pricing-driven resource allocation in the computing continuum. The workflow generates topology-specific pricing models, creates constrained problem instances, and delegates optimization to PRIME through its REST API.

The project is intended for research-grade experimentation and reproducibility.

GitHub URL: https://anonymous.4open.science/r/services-allocation

Table of Contents

1. Project Structure
2. How to Reproduce the Experiment
3. API of pricing_driven_resource_allocation
4. Data and Outputs
5. License & Disclaimer

Project Structure

The repository is organized as follows (main elements only):

services-allocation/
├── config/
│   └── experiment_configuration.yml      # Scenario definitions (small/medium/large)
├── docker-compose.yml                    # PRIME analysis API service (port 3000)
├── evaluation.ipynb                      # End-to-end experimental pipeline
├── eua-dataset/
│   ├── edge-servers/                     # Input edge-node datasets
│   └── users/                            # Input user-location datasets
├── iPricing/
│   ├── iPricing.proto                    # Pricing model schema
│   └── model/                            # Generated Python protobuf module
├── pricing_driven_resource_allocation/    # Core Python package
│   ├── __init__.py
│   ├── optimize.py                       # PRIME API client and polling loop
│   ├── dataset/
│   │   ├── load.py                       # Dataset loading utilities
│   │   ├── transform.py                  # Filtering and resource assignment
│   │   └── save_results.py               # Results persistence (CSV)
│   ├── generators/
│   │   ├── topology.py                   # Topology synthesis per scenario
│   │   ├── pricing.py                    # Pricing YAML generation
│   │   ├── problem_instance.py           # Request-constrained instance construction
│   │ &nbs