182 research outputs found

    Optimal Vertex Fault Tolerant Spanners (for fixed stretch)

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    A kk-spanner of a graph GG is a sparse subgraph HH whose shortest path distances match those of GG up to a multiplicative error kk. In this paper we study spanners that are resistant to faults. A subgraph HβŠ†GH \subseteq G is an ff vertex fault tolerant (VFT) kk-spanner if Hβˆ–FH \setminus F is a kk-spanner of Gβˆ–FG \setminus F for any small set FF of ff vertices that might "fail." One of the main questions in the area is: what is the minimum size of an ff fault tolerant kk-spanner that holds for all nn node graphs (as a function of ff, kk and nn)? This question was first studied in the context of geometric graphs [Levcopoulos et al. STOC '98, Czumaj and Zhao SoCG '03] and has more recently been considered in general undirected graphs [Chechik et al. STOC '09, Dinitz and Krauthgamer PODC '11]. In this paper, we settle the question of the optimal size of a VFT spanner, in the setting where the stretch factor kk is fixed. Specifically, we prove that every (undirected, possibly weighted) nn-node graph GG has a (2kβˆ’1)(2k-1)-spanner resilient to ff vertex faults with Ok(f1βˆ’1/kn1+1/k)O_k(f^{1 - 1/k} n^{1 + 1/k}) edges, and this is fully optimal (unless the famous Erdos Girth Conjecture is false). Our lower bound even generalizes to imply that no data structure capable of approximating distGβˆ–F(s,t)dist_{G \setminus F}(s, t) similarly can beat the space usage of our spanner in the worst case. We also consider the edge fault tolerant (EFT) model, defined analogously with edge failures rather than vertex failures. We show that the same spanner upper bound applies in this setting. Our data structure lower bound extends to the case k=2k=2 (and hence we close the EFT problem for 33-approximations), but it falls to Ξ©(f1/2βˆ’1/(2k)β‹…n1+1/k)\Omega(f^{1/2 - 1/(2k)} \cdot n^{1 + 1/k}) for kβ‰₯3k \ge 3. We leave it as an open problem to close this gap.Comment: To appear in SODA 201

    Constructing Light Spanners Deterministically in Near-Linear Time

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    Graph spanners are well-studied and widely used both in theory and practice. In a recent breakthrough, Chechik and Wulff-Nilsen [Shiri Chechik and Christian Wulff-Nilsen, 2018] improved the state-of-the-art for light spanners by constructing a (2k-1)(1+epsilon)-spanner with O(n^(1+1/k)) edges and O_epsilon(n^(1/k)) lightness. Soon after, Filtser and Solomon [Arnold Filtser and Shay Solomon, 2016] showed that the classic greedy spanner construction achieves the same bounds. The major drawback of the greedy spanner is its running time of O(mn^(1+1/k)) (which is faster than [Shiri Chechik and Christian Wulff-Nilsen, 2018]). This makes the construction impractical even for graphs of moderate size. Much faster spanner constructions do exist but they only achieve lightness Omega_epsilon(kn^(1/k)), even when randomization is used. The contribution of this paper is deterministic spanner constructions that are fast, and achieve similar bounds as the state-of-the-art slower constructions. Our first result is an O_epsilon(n^(2+1/k+epsilon\u27)) time spanner construction which achieves the state-of-the-art bounds. Our second result is an O_epsilon(m + n log n) time construction of a spanner with (2k-1)(1+epsilon) stretch, O(log k * n^(1+1/k) edges and O_epsilon(log k * n^(1/k)) lightness. This is an exponential improvement in the dependence on k compared to the previous result with such running time. Finally, for the important special case where k=log n, for every constant epsilon>0, we provide an O(m+n^(1+epsilon)) time construction that produces an O(log n)-spanner with O(n) edges and O(1) lightness which is asymptotically optimal. This is the first known sub-quadratic construction of such a spanner for any k = omega(1). To achieve our constructions, we show a novel deterministic incremental approximate distance oracle. Our new oracle is crucial in our construction, as known randomized dynamic oracles require the assumption of a non-adaptive adversary. This is a strong assumption, which has seen recent attention in prolific venues. Our new oracle allows the order of the edge insertions to not be fixed in advance, which is critical as our spanner algorithm chooses which edges to insert based on the answers to distance queries. We believe our new oracle is of independent interest

    Sparse Fault-Tolerant BFS Trees

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    This paper addresses the problem of designing a sparse {\em fault-tolerant} BFS tree, or {\em FT-BFS tree} for short, namely, a sparse subgraph TT of the given network GG such that subsequent to the failure of a single edge or vertex, the surviving part Tβ€²T' of TT still contains a BFS spanning tree for (the surviving part of) GG. Our main results are as follows. We present an algorithm that for every nn-vertex graph GG and source node ss constructs a (single edge failure) FT-BFS tree rooted at ss with O(n \cdot \min\{\Depth(s), \sqrt{n}\}) edges, where \Depth(s) is the depth of the BFS tree rooted at ss. This result is complemented by a matching lower bound, showing that there exist nn-vertex graphs with a source node ss for which any edge (or vertex) FT-BFS tree rooted at ss has Ξ©(n3/2)\Omega(n^{3/2}) edges. We then consider {\em fault-tolerant multi-source BFS trees}, or {\em FT-MBFS trees} for short, aiming to provide (following a failure) a BFS tree rooted at each source s∈Ss\in S for some subset of sources SβŠ†VS\subseteq V. Again, tight bounds are provided, showing that there exists a poly-time algorithm that for every nn-vertex graph and source set SβŠ†VS \subseteq V of size Οƒ\sigma constructs a (single failure) FT-MBFS tree Tβˆ—(S)T^*(S) from each source si∈Ss_i \in S, with O(Οƒβ‹…n3/2)O(\sqrt{\sigma} \cdot n^{3/2}) edges, and on the other hand there exist nn-vertex graphs with source sets SβŠ†VS \subseteq V of cardinality Οƒ\sigma, on which any FT-MBFS tree from SS has Ξ©(Οƒβ‹…n3/2)\Omega(\sqrt{\sigma}\cdot n^{3/2}) edges. Finally, we propose an O(log⁑n)O(\log n) approximation algorithm for constructing FT-BFS and FT-MBFS structures. The latter is complemented by a hardness result stating that there exists no Ξ©(log⁑n)\Omega(\log n) approximation algorithm for these problems under standard complexity assumptions
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