TY - GEN
T1 - A simple deterministic distributed MST algorithm, with near-optimal time and message complexities
AU - Elkin, Michael
N1 - Publisher Copyright: © 2017 Association for Computing Machinery.
PY - 2017/7/26
Y1 - 2017/7/26
N2 - Distributed minimum spanning tree (MST) problem is one of the most central and fundamental problems in distributed graph algorithms. Kutten and Peleg [KP98] devised an algorithm with running time O(D + √n · log∗ n), where D is the hop-diameter of the input n-vertexm-edge graph, and with message complexity O(m + n3/2). Peleg and Rubinovich [PR99] showed that the running time of the algorithm of [KP98] is essentially tight, and asked if one can achieve near-optimal running time together with near-optimal message complexity. In a recent breakthrough, Pandurangan et al. [PRS16] answered this question in the affirmative, and devised a randomized algorithm with time Õ (D + √n) and message complexity Õ (m). They asked if such a simultaneous time- and message-optimality can be achieved by a deterministic algorithm. In this paper, building upon the work of [PRS16], we answer this question in the affirmative, and devise a deterministic algorithm that computes MST in time O((D + √n) · log n), using O(m · log n + n log n · log∗ n) messages. The polylogarithmic factors in the time and message complexities of our algorithm are significantly smaller than the respective factors in the result of [PRS16]. Also, our algorithm and its analysis are very simple and self-contained, as opposed to rather complicated previous sublinear-time algorithms [GKP98, KP98, Elk04b, PRS16].
AB - Distributed minimum spanning tree (MST) problem is one of the most central and fundamental problems in distributed graph algorithms. Kutten and Peleg [KP98] devised an algorithm with running time O(D + √n · log∗ n), where D is the hop-diameter of the input n-vertexm-edge graph, and with message complexity O(m + n3/2). Peleg and Rubinovich [PR99] showed that the running time of the algorithm of [KP98] is essentially tight, and asked if one can achieve near-optimal running time together with near-optimal message complexity. In a recent breakthrough, Pandurangan et al. [PRS16] answered this question in the affirmative, and devised a randomized algorithm with time Õ (D + √n) and message complexity Õ (m). They asked if such a simultaneous time- and message-optimality can be achieved by a deterministic algorithm. In this paper, building upon the work of [PRS16], we answer this question in the affirmative, and devise a deterministic algorithm that computes MST in time O((D + √n) · log n), using O(m · log n + n log n · log∗ n) messages. The polylogarithmic factors in the time and message complexities of our algorithm are significantly smaller than the respective factors in the result of [PRS16]. Also, our algorithm and its analysis are very simple and self-contained, as opposed to rather complicated previous sublinear-time algorithms [GKP98, KP98, Elk04b, PRS16].
UR - http://www.scopus.com/inward/record.url?scp=85027846648&partnerID=8YFLogxK
U2 - 10.1145/3087801.3087823
DO - 10.1145/3087801.3087823
M3 - Conference contribution
T3 - Proceedings of the Annual ACM Symposium on Principles of Distributed Computing
SP - 157
EP - 163
BT - PODC 2017 - Proceedings of the ACM Symposium on Principles of Distributed Computing
T2 - 36th ACM Symposium on Principles of Distributed Computing, PODC 2017
Y2 - 25 July 2017 through 27 July 2017
ER -