1.1.1

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.

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

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
│   │   ├── client_demand.py              # Demand modeling by application class
│   │   └── request.py                    # Request payload builder
│   └── utils/
│       ├── geometrical_utils.py          # Spatial computations
│       └── yaml_utils.py                 # YAML <-> protobuf conversion helpers
├── results/
│   ├── results.csv                       # Aggregated optimization outcomes
│   └── figures/                          # Publication-ready plots
├── synthetic-dataset/
│   ├── data/
│   └── synthetic-topologies/             # 9600 generated topologies and instances
├── requirements.txt
├── setup.py
└── README.md

How to Reproduce the Experiment

Warning

The execution of this experiment can be computationally intensive and may require several hours, particularly when running the large-scale scenario. Before starting the procedure, ensure that adequate computational resources and sufficient uninterrupted execution time are available.

For reference, the experiment was conducted on a workstation equipped with an Apple Silicon M4 Pro processor and 24 gigabytes of main memory, where the complete execution required approximately ten hours.

1. Prerequisites

• Python 3.10+ recommended
• Docker and Docker Compose
• Protocol Buffers compiler (protoc)
• Jupyter (to execute evaluation.ipynb)

2. Clone and install

To clone the repository, use:

git clone <repository-url>

or download the zip file from the anonymous repository.

cd resource-allocation
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .

3. Confirm port 3000 is available

Before launching PRIME, verify that no process is currently bound to TCP port 3000:

lsof -i :3000

If the command returns any process, release the port before continuing.

4. Start PRIME from the project root

From the repository root, launch the PRIME service exactly as follows:

docker-compose up -d

Then verify health:

curl http://localhost:3000/health

The notebook uses PRIME_INSTANCE_URL = "http://localhost:3000/api/v1/", therefore PRIME must be reachable on port 3000. If you run PRIME on any other port, remember to update the URL in the first cell of evaluation.ipynb accordingly.

5. Execute the experiment pipeline

Run the notebook and execute all cells in order:

jupyter notebook evaluation.ipynb

Pipeline stages implemented in the notebook:

1. Initialize constants, paths, offer configuration, and resources.
2. Load and validate config/experiment_configuration.yml.
3. Load and preprocess the EUA edge dataset.
4. Generate topologies for each scenario and repetition.
5. Build pricing files and scenario-specific problem instances.
6. Invoke PRIME optimization through pdsa.optimize(...).
7. Persist execution metrics in results/results.csv and generate figures in results/figures/.

6. Stop services after completion

docker-compose down

API of pricing_driven_resource_allocation

The package exposes four public namespaces at the top level:

• pdsa.dataset
• pdsa.generators
• pdsa.utils
• pdsa.optimize

Top-level API

import pricing_driven_resource_allocation as pdsa

pdsa.optimize(...)
pdsa.dataset.*
pdsa.generators.*
pdsa.utils.*

pdsa.dataset

• load_devices_dataframe(path: str) -> pandas.DataFrame
  Loads the raw edge device CSV and standardizes column names.
• load_client_locations_dataframe(path: str) -> pandas.DataFrame
  Loads and normalizes client geolocation data.
• filter_devices_by_vendors(devices_df: pandas.DataFrame, vendors_to_consider: list) -> pandas.DataFrame
  Filters devices by provider tokens in device names and adds a normalized provider field.
• assign_device_resources(df: pandas.DataFrame, config: dict | None = None, seed: int | None = None) -> pandas.DataFrame
  Assigns capacities, prices, global groups, and device classes according to configurable stochastic rules.
• save_results_to_csv(result_obj: dict, scenario_id: str, RESULTS_DIR: str, filename: str = "results.csv", include_filter: bool = True) -> None
  Stores optimization outcomes and filter metadata in CSV format.

pdsa.generators

• topology(...) -> tuple[pandas.DataFrame, str]
  Creates a topology constrained by center, radius, providers, and device count; writes devices.csv, metadata.json, and map.html.

  Core signature:

  pdsa.generators.topology(
          lat: float,
          long: float,
          rad: float,
          devices_df: pandas.DataFrame,
          topologies_result_dir: str,
          resources_to_consider: list[str],
          number_of_providers: int | None = None,
          allowed_groups: list[int] | None = None,
          number_of_devices: int | None = None,
          center_elevation: float = 0.0,
          options: dict | None = None,
  )

• pricing_from_topology(...) -> str
  Converts a generated topology into a pricing YAML instance (pricing.yml).

• compatible_provider_groups_from_offer(topology_offer: dict) -> list[list[str]]
  Computes compatible provider groups from exclusion constraints.

• problem_instance(instance_pricing, request: dict, topologies_result_dir: str, unlimited_value: int = 100000000, options: dict = ...) -> tuple
  Generates a request-constrained pricing instance and a solver filter.

• request(topology_demand: dict, topology_request: dict, users_demand: dict, resources_to_consider: list[str], currency: str = "USD", resource_mapping: dict | None = None) -> dict
  Builds normalized request payloads for problem-instance generation.

• client_demand.calculate_resources(...) -> dict
  Estimates resource demand from user volume and application behavior profiles.

pdsa.utils

• yaml_to_pricing_proto(yaml_path: str, message_type)
  Parses pricing YAML into protobuf objects.
• pricing_proto_to_yaml(pricing_obj, yaml_path: str, options: dict | None = None) -> None
  Serializes protobuf pricing instances into YAML.
• find_identical_addons(pricing_obj) -> list[tuple[str, str]]
  Detects structurally identical add-ons.
• haversine(...) -> float
• distance_3d(...) -> float
• point_in_polygon(...) -> bool
• distance_to_farthest_edge(...) -> float

pdsa.optimize

• optimize(prime_instance_url: str, pricing_instance_path: str, request: dict, poll_interval_seconds: float = 0.1, timeout_seconds: float | None = 600.0, session: requests.Session | None = None) -> dict

Behavior:

1. Submits a multipart optimization job to POST {prime_instance_url}/pricing/analysis.
2. Polls GET {prime_instance_url}/pricing/analysis/{jobId} until terminal status.
3. Returns the final payload (COMPLETED or FAILED).

Data and Outputs

• Input datasets:
  • eua-dataset/edge-servers/site.csv
  • eua-dataset/users/users-aus.csv
• Scenario specification:
  • config/experiment_configuration.yml
• Generated artifacts:
  • synthetic-dataset/synthetic-topologies/<topology_id>/devices.csv
  • synthetic-dataset/synthetic-topologies/<topology_id>/pricing.yml
  • synthetic-dataset/synthetic-topologies/<topology_id>/problem_instance_pricing.yml
  • results/results.csv
  • results/figures/*.png

⚠️ Disclaimer & License

LICENSE

This project is licensed under the MIT License. See LICENSE for details

DISCLAIMER

This tool is part of ongoing research by the ISA group in pricing-driven development and operation. It is in an early stage and ...

1.1.0

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.

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

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
│   │   ├── client_demand.py              # Demand modeling by application class
│   │   └── request.py                    # Request payload builder
│   └── utils/
│       ├── geometrical_utils.py          # Spatial computations
│       └── yaml_utils.py                 # YAML <-> protobuf conversion helpers
├── results/
│   ├── results.csv                       # Aggregated optimization outcomes
│   └── figures/                          # Publication-ready plots
├── synthetic-dataset/
│   ├── data/
│   └── synthetic-topologies/             # 9600 generated topologies and instances
├── requirements.txt
├── setup.py
└── README.md

How to Reproduce the Experiment

Warning

The execution of this experiment can be computationally intensive and may require several hours, particularly when running the large-scale scenario. Before starting the procedure, ensure that adequate computational resources and sufficient uninterrupted execution time are available.

For reference, the experiment was conducted on a workstation equipped with an Apple Silicon M4 Pro processor and 24 gigabytes of main memory, where the complete execution required approximately ten hours.

1. Prerequisites

• Python 3.10+ recommended
• Docker and Docker Compose
• Protocol Buffers compiler (protoc)
• Jupyter (to execute evaluation.ipynb)

2. Clone and install

git clone <repository-url>
cd services-allocation
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .

3. Confirm port 3000 is available

Before launching PRIME, verify that no process is currently bound to TCP port 3000:

lsof -i :3000

If the command returns any process, release the port before continuing.

4. Start PRIME from the project root

From the repository root, launch the PRIME service exactly as follows:

docker-compose up -d

Then verify health:

curl http://localhost:3000/health

The notebook uses PRIME_INSTANCE_URL = "http://localhost:3000/api/v1/", therefore PRIME must be reachable on port 3000. If you run PRIME on any other port, remember to update the URL in the first cell of evaluation.ipynb accordingly.

5. Execute the experiment pipeline

Run the notebook and execute all cells in order:

jupyter notebook evaluation.ipynb

Pipeline stages implemented in the notebook:

1. Initialize constants, paths, offer configuration, and resources.
2. Load and validate config/experiment_configuration.yml.
3. Load and preprocess the EUA edge dataset.
4. Generate topologies for each scenario and repetition.
5. Build pricing files and scenario-specific problem instances.
6. Invoke PRIME optimization through pdsa.optimize(...).
7. Persist execution metrics in results/results.csv and generate figures in results/figures/.

6. Stop services after completion

docker-compose down

API of pricing_driven_resource_allocation

The package exposes four public namespaces at the top level:

• pdsa.dataset
• pdsa.generators
• pdsa.utils
• pdsa.optimize

Top-level API

import pricing_driven_resource_allocation as pdsa

pdsa.optimize(...)
pdsa.dataset.*
pdsa.generators.*
pdsa.utils.*

pdsa.dataset

• load_devices_dataframe(path: str) -> pandas.DataFrame
  Loads the raw edge device CSV and standardizes column names.
• load_client_locations_dataframe(path: str) -> pandas.DataFrame
  Loads and normalizes client geolocation data.
• filter_devices_by_vendors(devices_df: pandas.DataFrame, vendors_to_consider: list) -> pandas.DataFrame
  Filters devices by provider tokens in device names and adds a normalized provider field.
• assign_device_resources(df: pandas.DataFrame, config: dict | None = None, seed: int | None = None) -> pandas.DataFrame
  Assigns capacities, prices, global groups, and device classes according to configurable stochastic rules.
• save_results_to_csv(result_obj: dict, scenario_id: str, RESULTS_DIR: str, filename: str = "results.csv", include_filter: bool = True) -> None
  Stores optimization outcomes and filter metadata in CSV format.

pdsa.generators

• topology(...) -> tuple[pandas.DataFrame, str]
  Creates a topology constrained by center, radius, providers, and device count; writes devices.csv, metadata.json, and map.html.

  Core signature:

  pdsa.generators.topology(
          lat: float,
          long: float,
          rad: float,
          devices_df: pandas.DataFrame,
          topologies_result_dir: str,
          resources_to_consider: list[str],
          number_of_providers: int | None = None,
          allowed_groups: list[int] | None = None,
          number_of_devices: int | None = None,
          center_elevation: float = 0.0,
          options: dict | None = None,
  )

• pricing_from_topology(...) -> str
  Converts a generated topology into a pricing YAML instance (pricing.yml).

• compatible_provider_groups_from_offer(topology_offer: dict) -> list[list[str]]
  Computes compatible provider groups from exclusion constraints.

• problem_instance(instance_pricing, request: dict, topologies_result_dir: str, unlimited_value: int = 100000000, options: dict = ...) -> tuple
  Generates a request-constrained pricing instance and a solver filter.

• request(topology_demand: dict, topology_request: dict, users_demand: dict, resources_to_consider: list[str], currency: str = "USD", resource_mapping: dict | None = None) -> dict
  Builds normalized request payloads for problem-instance generation.

• client_demand.calculate_resources(...) -> dict
  Estimates resource demand from user volume and application behavior profiles.

pdsa.utils

• yaml_to_pricing_proto(yaml_path: str, message_type)
  Parses pricing YAML into protobuf objects.
• pricing_proto_to_yaml(pricing_obj, yaml_path: str, options: dict | None = None) -> None
  Serializes protobuf pricing instances into YAML.
• find_identical_addons(pricing_obj) -> list[tuple[str, str]]
  Detects structurally identical add-ons.
• haversine(...) -> float
• distance_3d(...) -> float
• point_in_polygon(...) -> bool
• distance_to_farthest_edge(...) -> float

pdsa.optimize

• optimize(prime_instance_url: str, pricing_instance_path: str, request: dict, poll_interval_seconds: float = 0.1, timeout_seconds: float | None = 600.0, session: requests.Session | None = None) -> dict

Behavior:

1. Submits a multipart optimization job to POST {prime_instance_url}/pricing/analysis.
2. Polls GET {prime_instance_url}/pricing/analysis/{jobId} until terminal status.
3. Returns the final payload (COMPLETED or FAILED).

Data and Outputs

• Input datasets:
  • eua-dataset/edge-servers/site.csv
  • eua-dataset/users/users-aus.csv
• Scenario specification:
  • config/experiment_configuration.yml
• Generated artifacts:
  • synthetic-dataset/synthetic-topologies/<topology_id>/devices.csv
  • synthetic-dataset/synthetic-topologies/<topology_id>/pricing.yml
  • synthetic-dataset/synthetic-topologies/<topology_id>/problem_instance_pricing.yml
  • results/results.csv
  • results/figures/*.png

⚠️ Disclaimer & License

LICENSE

This project is licensed under the MIT License. See LICENSE for details

DISCLAIMER

This tool is part of ongoing research by the ISA group in pricing-driven development and operation. It is in an early stage and is not intended for production use. The ISA group does not accept responsibility for any issues or damages t...

1.0.0

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

This repository contains the implementation used to study pricing-driven resource allocation in the cloud 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.

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

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
│   │   ├── client_demand.py              # Demand modeling by application class
│   │   └── request.py                    # Request payload builder
│   └── utils/
│       ├── geometrical_utils.py          # Spatial computations
│       └── yaml_utils.py                 # YAML <-> protobuf conversion helpers
├── results/
│   ├── results.csv                       # Aggregated optimization outcomes
│   └── figures/                          # Publication-ready plots
├── synthetic-dataset/
│   ├── data/
│   └── synthetic-topologies/             # 9600 generated topologies and instances
├── requirements.txt
├── setup.py
└── README.md

How to Reproduce the Experiment

Warning

The execution of this experiment can be computationally intensive and may require several hours, particularly when running the large-scale scenario. Before starting the procedure, ensure that adequate computational resources and sufficient uninterrupted execution time are available.

For reference, the experiment was conducted on a workstation equipped with an Apple Silicon M4 Pro processor and 24 gigabytes of main memory, where the complete execution required approximately ten hours.

1. Prerequisites

• Python 3.10+ recommended
• Docker and Docker Compose
• Protocol Buffers compiler (protoc)
• Jupyter (to execute evaluation.ipynb)

2. Clone and install

git clone <repository-url>
cd services-allocation
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .

3. Confirm port 3000 is available

Before launching PRIME, verify that no process is currently bound to TCP port 3000:

lsof -i :3000

If the command returns any process, release the port before continuing.

4. Start PRIME from the project root

From the repository root, launch the PRIME service exactly as follows:

docker-compose up -d

Then verify health:

curl http://localhost:3000/health

The notebook uses PRIME_INSTANCE_URL = "http://localhost:3000/api/v1/", therefore PRIME must be reachable on port 3000. If you run PRIME on any other port, remember to update the URL in the first cell of evaluation.ipynb accordingly.

5. Execute the experiment pipeline

Run the notebook and execute all cells in order:

jupyter notebook evaluation.ipynb

Pipeline stages implemented in the notebook:

1. Initialize constants, paths, offer configuration, and resources.
2. Load and validate config/experiment_configuration.yml.
3. Load and preprocess the EUA edge dataset.
4. Generate topologies for each scenario and repetition.
5. Build pricing files and scenario-specific problem instances.
6. Invoke PRIME optimization through pdsa.optimize(...).
7. Persist execution metrics in results/results.csv and generate figures in results/figures/.

6. Stop services after completion

docker-compose down

API of pricing_driven_resource_allocation

The package exposes four public namespaces at the top level:

• pdsa.dataset
• pdsa.generators
• pdsa.utils
• pdsa.optimize

Top-level API

import pricing_driven_resource_allocation as pdsa

pdsa.optimize(...)
pdsa.dataset.*
pdsa.generators.*
pdsa.utils.*

pdsa.dataset

• load_devices_dataframe(path: str) -> pandas.DataFrame
  Loads the raw edge device CSV and standardizes column names.
• load_client_locations_dataframe(path: str) -> pandas.DataFrame
  Loads and normalizes client geolocation data.
• filter_devices_by_vendors(devices_df: pandas.DataFrame, vendors_to_consider: list) -> pandas.DataFrame
  Filters devices by provider tokens in device names and adds a normalized provider field.
• assign_device_resources(df: pandas.DataFrame, config: dict | None = None, seed: int | None = None) -> pandas.DataFrame
  Assigns capacities, prices, global groups, and device classes according to configurable stochastic rules.
• save_results_to_csv(result_obj: dict, scenario_id: str, RESULTS_DIR: str, filename: str = "results.csv", include_filter: bool = True) -> None
  Stores optimization outcomes and filter metadata in CSV format.

pdsa.generators

• topology(...) -> tuple[pandas.DataFrame, str]
  Creates a topology constrained by center, radius, providers, and device count; writes devices.csv, metadata.json, and map.html.

  Core signature:

  pdsa.generators.topology(
          lat: float,
          long: float,
          rad: float,
          devices_df: pandas.DataFrame,
          topologies_result_dir: str,
          resources_to_consider: list[str],
          number_of_providers: int | None = None,
          allowed_groups: list[int] | None = None,
          number_of_devices: int | None = None,
          center_elevation: float = 0.0,
          options: dict | None = None,
  )

• pricing_from_topology(...) -> str
  Converts a generated topology into a pricing YAML instance (pricing.yml).

• compatible_provider_groups_from_offer(topology_offer: dict) -> list[list[str]]
  Computes compatible provider groups from exclusion constraints.

• problem_instance(instance_pricing, request: dict, topologies_result_dir: str, unlimited_value: int = 100000000, options: dict = ...) -> tuple
  Generates a request-constrained pricing instance and a solver filter.

• request(topology_demand: dict, topology_request: dict, users_demand: dict, resources_to_consider: list[str], currency: str = "USD", resource_mapping: dict | None = None) -> dict
  Builds normalized request payloads for problem-instance generation.

• client_demand.calculate_resources(...) -> dict
  Estimates resource demand from user volume and application behavior profiles.

pdsa.utils

• yaml_to_pricing_proto(yaml_path: str, message_type)
  Parses pricing YAML into protobuf objects.
• pricing_proto_to_yaml(pricing_obj, yaml_path: str, options: dict | None = None) -> None
  Serializes protobuf pricing instances into YAML.
• find_identical_addons(pricing_obj) -> list[tuple[str, str]]
  Detects structurally identical add-ons.
• haversine(...) -> float
• distance_3d(...) -> float
• point_in_polygon(...) -> bool
• distance_to_farthest_edge(...) -> float

pdsa.optimize

• optimize(prime_instance_url: str, pricing_instance_path: str, request: dict, poll_interval_seconds: float = 0.1, timeout_seconds: float | None = 600.0, session: requests.Session | None = None) -> dict

Behavior:

1. Submits a multipart optimization job to POST {prime_instance_url}/pricing/analysis.
2. Polls GET {prime_instance_url}/pricing/analysis/{jobId} until terminal status.
3. Returns the final payload (COMPLETED or FAILED).

Data and Outputs

• Input datasets:
  • eua-dataset/edge-servers/site.csv
  • eua-dataset/users/users-aus.csv
• Scenario specification:
  • config/experiment_configuration.yml
• Generated artifacts:
  • synthetic-dataset/synthetic-topologies/<topology_id>/devices.csv
  • synthetic-dataset/synthetic-topologies/<topology_id>/pricing.yml
  • synthetic-dataset/synthetic-topologies/<topology_id>/problem_instance_pricing.yml
  • results/results.csv
  • results/figures/*.png

⚠️ Disclaimer & License

LICENSE

This project is licensed under the MIT License. See LICENSE for details

DISCLAIMER

This tool is part of ongoing research by the ISA group in pricing-driven development and operation. It is in an early stage and is not intended for production use. The ISA group does not accept responsibility for any issues or damages that may ...