  Given an undirected graph G, the edge orientation problem asks for assigning
a direction to each edge to convert G into a directed graph. The aim is to
minimize the maximum out degree of a vertex in the resulting directed graph.
This problem, which is solvable in polynomial time, arises in many
applications. An ongoing challenge in edge orientation algorithms is their
scalability, particularly in handling large-scale networks with millions or
billions of edges efficiently. We propose a novel algorithmic framework based
on finding and manipulating simple paths to face this challenge. Our framework
is based on an existing algorithm and allows many algorithmic choices. By
carefully exploring these choices and engineering the underlying algorithms, we
obtain an implementation which is more efficient and scalable than the current
state-of-the-art. Our experiments demonstrate significant performance
improvements compared to state-of-the-art solvers. On average our algorithm is
6.59 times faster when compared to the state-of-the-art.
