Brief Announcement: Efficient Self-Stabilizing 1-Maximal Matching Algorithm for Arbitrary Networks
Résumé
We present a new self-stabilizing 1-maximal matching algorithm that works under the distributed unfair daemon for arbitrarily shaped networks. The 1-maximal matching is a 2/3-approximation of a maximum matching, a significant improvement over the 1/2-approximation that is guaranteed by a maximal matching. Our algorithm is efficient (its stabilization time is $O(e)$ moves, where $e$ denotes the number of edges in the network). Besides, our algorithm is optimal with respect to identifiers locality (we assume node identifiers are distinct up to distance three, a necessary condition to withstand arbitrary networks). The proposed algorithm closes the complexity gap between two recent works: Inoue et al. presented a 1-maximal matching algorithm that is $O(e)$ moves but requires the network topology not to contain a cycle of size of multiple of three ; Cohen et al. consider arbitrary topology networks but requires $O(n^3)$ moves to stabilize (where $n$ denotes the number of nodes in the network). Our solution preserves the better complexity of $O(e)$ moves, yet considers arbitrary networks, demonstrating that previous restrictions were unnecessary to preserve complexity results.