Consensus Needs Broadcast in Noiseless Models but can be Exponentially Easier in the Presence of Noise

Consensus and Broadcast are two fundamental problems in distributed computing, whose solutions have several applications. Intuitively, Consensus should be no harder than Broadcast, and this can be rigorously established in several models. Can Consensus be easier than Broadcast? In models that allow noiseless communication, we prove a reduction of (a suitable variant of) Broadcast to binary Consensus, that preserves the communication model and all complexity parameters such as randomness, number of rounds, communication per round, etc., while there is a loss in the success probability of the protocol. Using this reduction, we get, among other applications, the first logarithmic lower bound on the number of rounds needed to achieve Consensus in the uniform GOSSIP model on the complete graph. The lower bound is tight and, in this model, Consensus and Broadcast are equivalent. We then turn to distributed models with noisy communication channels that have been studied in the context of some bio-inspired systems. In such models, only one noisy bit is exchanged when a communication channel is established between two nodes, and so one cannot easily simulate a noiseless protocol by using error-correcting codes. An $\Omega(\epsilon^{-2} n)$ lower bound on the number of rounds needed for Broadcast is proved by Boczkowski et al. [PLOS Comp. Bio. 2018] in one such model (noisy uniform PULL, where $\epsilon$ is a parameter that measures the amount of noise). In such model, we prove a new $\Theta(\epsilon^{-2} n \log n)$ bound for Broadcast and a $\Theta(\epsilon^{-2} \log n)$ bound for binary Consensus, thus establishing an exponential gap between the number of rounds necessary for Consensus versus Broadcast

Path-Fault-Tolerant Approximate Shortest-Path Trees

Let $G=(V,E)$ be an $n$-nodes non-negatively real-weighted undirected graph. In this paper we show how to enrich a {\em single-source shortest-path tree} (SPT) of $G$ with a \emph{sparse} set of \emph{auxiliary} edges selected from $E$, in order to create a structure which tolerates effectively a \emph{path failure} in the SPT. This consists of a simultaneous fault of a set $F$ of at most $f$ adjacent edges along a shortest path emanating from the source, and it is recognized as one of the most frequent disruption in an SPT. We show that, for any integer parameter $k \geq 1$, it is possible to provide a very sparse (i.e., of size $O(kn\cdot f^{1+1/k})$) auxiliary structure that carefully approximates (i.e., within a stretch factor of $(2k-1)(2|F|+1)$) the true shortest paths from the source during the lifetime of the failure. Moreover, we show that our construction can be further refined to get a stretch factor of $3$ and a size of $O(n \log n)$ for the special case $f=2$, and that it can be converted into a very efficient \emph{approximate-distance sensitivity oracle}, that allows to quickly (even in optimal time, if $k=1$) reconstruct the shortest paths (w.r.t. our structure) from the source after a path failure, thus permitting to perform promptly the needed rerouting operations. Our structure compares favorably with previous known solutions, as we discuss in the paper, and moreover it is also very effective in practice, as we assess through a large set of experiments.Comment: 21 pages, 3 figures, SIROCCO 201

Effective Edge-Fault-Tolerant Single-Source Spanners via Best (or Good) Swap Edges

Computing \emph{all best swap edges} (ABSE) of a spanning tree $T$ of a given $n$-vertex and $m$-edge undirected and weighted graph $G$ means to select, for each edge $e$ of $T$, a corresponding non-tree edge $f$, in such a way that the tree obtained by replacing $e$ with $f$ enjoys some optimality criterion (which is naturally defined according to some objective function originally addressed by $T$). Solving efficiently an ABSE problem is by now a classic algorithmic issue, since it conveys a very successful way of coping with a (transient) \emph{edge failure} in tree-based communication networks: just replace the failing edge with its respective swap edge, so as that the connectivity is promptly reestablished by minimizing the rerouting and set-up costs. In this paper, we solve the ABSE problem for the case in which $T$ is a \emph{single-source shortest-path tree} of $G$, and our two selected swap criteria aim to minimize either the \emph{maximum} or the \emph{average stretch} in the swap tree of all the paths emanating from the source. Having these criteria in mind, the obtained structures can then be reviewed as \emph{edge-fault-tolerant single-source spanners}. For them, we propose two efficient algorithms running in $O(m n +n^2 \log n)$ and $O(m n \log \alpha(m,n))$ time, respectively, and we show that the guaranteed (either maximum or average, respectively) stretch factor is equal to 3, and this is tight. Moreover, for the maximum stretch, we also propose an almost linear $O(m \log \alpha(m,n))$ time algorithm computing a set of \emph{good} swap edges, each of which will guarantee a relative approximation factor on the maximum stretch of $3/2$ (tight) as opposed to that provided by the corresponding BSE. Surprisingly, no previous results were known for these two very natural swap problems.Comment: 15 pages, 4 figures, SIROCCO 201

Efficient Truthful Mechanisms for the Single-Source Shortest Paths Tree Problem

Let a communication network be modelled by an undirected graph G=(V,E) of n nodes and m edges, and assume that each edge is controlled by a selfish agent. In this paper we analyze the problem of designing a truthful mechanism for computing one of the most used structures in communication networks, i.e., the single-source shortest paths tree. More precisely, we will show that under various realistic agents’ behavior scenarios, it can be guaranteed not only the existence, but also the efficiency (in terms of running time complexity) of such mechanisms. In particular, for the fundamental case in which the problem is utilitarian, we will show that a truthful mechanism can be computed in O(mn log α(m,n)) time, where α(m,n) is the classic inverse of the Ackermann’s function

Exact and Approximate Truthful Mechanisms for the Shortest Paths Tree Problem

Let a communication network be modeled by an undirected graph G=(V,E) of n nodes and m edges, and assume that edges are controlled by selfish agents. In this paper we analyze the problem of designing a truthful mechanism for computing one of the most popular structures in communication networks, i.e., the single-source shortest paths tree. More precisely, we will study several realistic scenarios, in which each agent can own either a single or multiple edges of G. In particular, for the single-edge case, we will show that: (i) in the classic utilitarian case, the problem can be solved efficiently in O(mnlogα(m,n)) time, where ±(m,n) is the inverse of the Ackermanns function; (ii) in a meaningful non-utilitarian case, namely that in which agents valuation functions only depend on the edge lengths, the problem can be solved in O(m+nlogn) time. Conversely, for the multiple-edges case, we will show in the utilitarian case an O(mP+nPlogn) time truthful mechanism, where P=O(n) denotes the number of agents participating in the solution, while in the same non-utilitarian case we will prove a general lower bound to the approximation ratio that can be achieved by any truthful mechanism, by showing that no c-approximate mechanism can exist, for any fixed c < 5+√13 / 3+√13

A Truthful (2-2/k)-Approximation Mechanism for the Steiner Tree Problem with k Terminals

Let a communication network be modelled by an undirected graph G = ( V, E) of n nodes and m edges, and assume that each edge is owned by a selfish agent, which establishes the cost of using her edge by pursuing only her personal utility. In such a non-cooperative setting, we aim at designing a truthful mechanism for the problem of finding a minimum Steiner tree of G. Since no poly-time computable exact truthful mechanism can exist for such a problem (unless P=NP), we provide a truthful (2 - 2/k)-approximation mechanism which can be computed in O((n + k2)m log α(m, n)) time, where k is the number of terminal nodes, and α(.,.) is the classic inverse of the Ackermann's function. This compares favorably with the previous known O(kn(m + n log n)) time and 2-approximate truthful mechanism for solving the problem

Finding Best Swap Edges Minimizing the Routing Cost of a Spanning Tree

Given an n-node, undirected and 2-edge-connected graph G = (V,E) with positive real weights on its m edges, given a set of k source nodes S ⊆ V, and given a spanning tree T of G, the routing cost of T w.r.t. S is the sum of the distances in T from every source s ∈ S to all the other nodes of G. If an edge e of T undergoes a transient failure and connectivity needs to be promptly reestablished, then to reduce set-up and rerouting costs it makes sense to temporarily replace e by means of a swap edge, i.e., an edge in G reconnecting the two subtrees of T induced by the removal of e. Then, a best swap edge for e is a swap edge which minimizes the routing cost of the tree obtained after the swapping. As a natural extension, the all-best swap edges problem is that of finding a best swap edge for every edge of T. Such a problem has been recently solved in O(mn) time and linear space for arbitrary k, and in O(n^2 + mlogn) time and O(n^2) space for the special case k = 2. In this paper, we are interested to the prominent cases k = O(1) and k = n, which model realistic communication paradigms. For these cases, we present a linear space and \{tilde O}(m) time algorithm, and thus we improve both the above running times (but for quite dense graphs in the case k = 2, for which however it is noticeable we make use of only linear space). Moreover, we provide an accurate analysis showing that when k = n, the obtained swap tree is effective in terms of routing cost

Improved Approximability and Non-approximability Results for Graph Diameter Decreasing Problems

In this paper we study two variants of the problem of adding edges to a graph so as to reduce the resulting diameter. More precisely, given a graph G = (V,E), and two positive integers D and B, the Minimum-Cardinality Bounded-Diameter Edge Addition (MCBD) problem is to find a minimum cardinality set F of edges to be added to G in such a way that the diameter of G + F is less than or equal to D, while the Bounded-Cardinality Minimum-Diameter Edge Addition (BCMD) problem is to find a set F of B edges to be added to G in such a way that the diameter of G + F is minimized. Both problems are well known to be NP-hard, as well as approximable within O(logn logD) and 4 (up to an additive term of 2), respectively. In this paper, we improve these long-standing approximation ratios to O(logn) and to 2 (up to an additive term of 2), respectively. As a consequence, we close, in an asymptotic sense, the gap on the approximability of the MCBD problem, which was known to be not approximable within c logn, for some constant c > 0, unless P=NP. Remarkably, as we further show in the paper, our approximation ratio remains asymptotically tight even if we allow for a solution whose diameter is optimal up to a multiplicative factor approaching 5/3. On the other hand, on the positive side, we show that at most twice of the minimal number of additional edges suffices to get at most twice of the required diameter

Improved approximability and non-approximability results for graph diameter decreasing problems

In this paper, we study two variants of the problem of adding edges to a graph so as to reduce the resulting diameter. More precisely, given a graph G=(V,E), and two positive integers D and B, the Minimum-Cardinality Bounded-Diameter Edge Addition (MCBD) problem is to find a minimum-cardinality set F of edges to be added to G in such a way that the diameter of G+F is less than or equal to D, while the Bounded-Cardinality Minimum-Diameter Edge Addition (BCMD) problem is to find a set F of B edges to be added to G in such a way that the diameter of G+F is minimized. Both problems are well known to be NP-hard, as well as approximable within O(lognlogD) and 4 (up to an additive term of 2), respectively. In this paper, we improve these long-standing approximation ratios to O(logn) and to 2 (up to an additive term of 2), respectively. As a consequence, we close, in an asymptotic sense, the gap on the approximability of MCBD, which was known to be not approximable within clogn, for some constant c>0, unless P=NP. Remarkably, as we further show in the paper, our approximation ratio remains asymptotically tight even if we allow for a solution whose diameter is optimal up to a multiplicative factor approaching 53. On the other hand, on the positive side, we show that at most twice of the minimal number of additional edges suffices to get at most twice of the required diameter. Some of our results extend to the edge-weighted version of the problems

Approximate Mechanisms for the Graphical TSP and Other Graph Traversal Problems

Let G = (V, E) be a graph modeling a network where each edge is owned by a selfish agent, which establishes the cost for traversing her edge (i.e., assigns a weight to her edge) by pursuing only her personal utility. In such a setting, we aim at designing approximate truthful mechanisms for several NP-hard traversal problems on G, like the graphical traveling salesman problem, the rural postman problem, and the mixed Chinese postman problem, either of which asks for using an edge of G several times, in general. Thus, in game-theoretic terms, these are one-parameter problems, but with a peculiarity: the work load of each agent is a natural number. In this paper we refine the classic notion of monotonicity of an algorithm so as to exactly capture this property, and we then provide a general mechanism design technique that guarantees this monotonicity and that allows to compute efficiently the corresponding payments. In this way, we show that the former two problems and the latter one admit a 3/2- and a 2-approximate truthful mechanism, respectively. Thus, for the first two problems we match the best known approximation ratios holding for their corresponding centralized versions, while for the third one we are only a 4/3-factor away from it