# A Distributed Method for Optimal Capacity Reservation

## TL;DR

This paper introduces a scalable distributed algorithm for optimal network capacity reservation, enabling efficient support of multiple flow scenarios while minimizing costs, by decomposing the large linear program into parallel flow problems and coordination steps.

## Contribution

It presents a novel distributed approach to solve the large-scale capacity reservation problem more efficiently than traditional linear programming methods.

## Key findings

- The distributed algorithm effectively supports multiple flow scenarios.
- It reduces computational complexity compared to monolithic LP solvers.
- The method scales to larger network problems.

## Abstract

We consider the problem of reserving link capacity in a network in such a way that any of a given set of flow scenarios can be supported. In the optimal capacity reservation problem, we choose the reserved link capacities to minimize the reservation cost. This problem reduces to a large linear program, with the number of variables and constraints on the order of the number of links times the number of scenarios. Small and medium size problems are within the capabilities of generic linear program solvers. We develop a more scalable, distributed algorithm for the problem that alternates between solving (in parallel) one flow problem per scenario, and coordination steps, which connect the individual flows and the reservation capacities.