DeDe is a general, scalable, and theoretically grounded optimization framework that accelerates large-scale resource allocation problems through a decouple-and-decompose approach.

Getting started

Hardware requirements

• Linux OS
• A multi-core CPU instance

Dependencies

• Python >= 3.8
• g++ (required by cvxpy)
• (optional) install pytest with pip install -U pytest

Installation

We have made DeDe available as a PyPI package! You can simply install it using pip:

pip install dede

• We recommend creating a Python virtual environment (e.g., venv or Conda) before installation.

Code structure

.
├── dede/                       # core source code
├── tests/                      # test suite
│     └── test_dede.py
└── examples/                   # example use cases
      ├── traffic_engineering/
      ├── cluster_scheduling/
      └── load_balancing/

Using DeDe

DeDe adopts a familiar interface from cvxpy, e.g., Variable(.), Minimize(.).

Key differences in DeDe:

• DeDe requires specifying separate resource_constraints and demand_constraints when constructing a problem.
• The solve(.) method includes additional parameters:
  • enable_dede: enables DeDe if True; defaults to cvxpy if False.
  • num_cpus: number of CPU cores (defaults to all available cores).
  • rho: ADMM parameter.
  • num_iter: maximum number of iterations; if not specified, DeDe stops if the accuracy improvement falls below 1%.

Toy examples

A toy example for resource allocation with DeDe is as follows:

import dede as dd
N, M = 100, 100

# Create allocation variables
x = dd.Variable((N, M), nonneg=True)

# Create the constraints
resource_constraints = [x[i,:].sum() >= i for i in range(N)]
demand_constraints = [x[:,j].sum() <= j for j in range(M)]

# Create an objective
objective = dd.Minimize(x.sum())

# Construct the problem
prob = dd.Problem(objective, resource_constraints, demand_constraints)

# Solve the problem with DeDe on 4 CPU cores
print(prob.solve(num_cpus=4, solver=dd.ECOS))

Another toy example is provided in tests/test_dede.py. To test these examples quickly, from the project root directory, run

./tests/test_dede.py

or, if pytest is installed:

pytest

Example output screenshots are provided in the assets folder.

Example use cases of DeDe

We provide three example applications of DeDe:

• Traffic engineering: a network flow optimization problem.
• Cluster scheduling: a resource allocation problem in cluster computing.
• Load balancing: a query balancing problem in distributed stores.

Please refer to examples/README.md for details.

Citation

If you use DeDe in your research, please cite our paper:

@inproceedings{dede,
    title={{Decouple and Decompose: Scaling Resource Allocation with DeDe}},
    author={Xu, Zhiying and Yu, Minlan and Yan, Francis Y.},
    booktitle={Proceedings of the USENIX OSDI 2025 Conference},
    month=jul,
    year={2025}
}