We present a self-stabilizing algorithm for the unison problem which is efficient in time, workload, and space in a weak model. Precisely, our algorithm is defined in the atomic-state model and works in anonymous asynchronous connected networks in which even local ports are unlabeled. It makes no assumption on the daemon and thus stabilizes under the weakest one: the distributed unfair daemon. In an n-node network of diameter D and assuming the knowledge B ≥ 2D+2, our algorithm only requires Θ(log(B)) bits per node and is fully polynomial as it stabilizes in at most 2D+2 rounds and O(min(n²B, n³)) moves. In particular, it is the first self-stabilizing unison for arbitrary asynchronous anonymous networks achieving an asymptotically optimal stabilization time in rounds using a bounded memory at each node. Furthermore, we show that our solution can be used to efficiently simulate synchronous self-stabilizing algorithms in asynchronous environments. For example, this simulation allows us to design a new state-of-the-art algorithm solving both the leader election and the BFS (Breadth-First Search) spanning tree construction in any identified connected network which, to the best of our knowledge, beats all existing solutions in the literature.

Distributed systems are ubiquitous, and their distributed nature make them particularly vulnerable to faults. Being able to automatically recover from these faults is of utmost importance, and self-stabilization is a general and lightweight approach to tackle this problem. However, fully asynchronous self-stabilizing algorithms (FASS) are notoriously difficult to design and prove. It thus makes sense to create and prove a transformer that turns synchronous algorithms into FASSes.

Abstract Petri nets constitute a well-studied model to verify and study concurrent systems, among others, and computing the coverability set is one of the most fundamental problems about Petri nets. Using the proof assistant Coq, we certified the correctness and termination of the MinCov algorithm by Finkel, Haddad, and Khmelnitsky (FOSSACS 2020). This algorithm is the most recent algorithm in the literature that computes the minimal basis of the coverability set, a problem known to be prone to subtle bugs. Apart from the intrinsic interest of a computer-checked proof, our certification provides new insights on the MinCov algorithm. In particular, we introduce as an intermediate algorithm a small-step variant of MinCov of independent interest.

This paper deals with the computation of d-dimensional multicriteria shortest paths. In a weighted graph with arc weights represented by vectors, the cost of a path is the vector sum of the weights of its arcs. For a given pair consisting of a source s and a destination t, a path P dominates a path Q if and only if P’s cost is component-wise smaller than or equal to Q’s cost. The set of Pareto paths, or Pareto set, from s to t is the set of paths that are not dominated. The computation time of the Pareto paths can be prohibitive whenever the set of Pareto paths is large. We propose in this article new algorithms to compute approximated Pareto paths in any dimension. For d = 2, we exhibit the first approximation algorithm, called Frame, whose output is guaranteed to be always a subset of the Pareto set. Finally, we provide a small experimental study in order to confirm the relevance of our Frame algorithm.

Two mobile robots are initially placed at the same point on an infinite line. Each robot may move on the line in either direction not exceeding its maximal speed. The robots need to find a stationary target placed at an unknown location on the line. The search is completed when both robots arrive at the target point. The target is discovered at the moment when either robot arrives at its position. The robot knowing the placement of the target may communicate it to the other robot. We look for the algorithm with the shortest possible search time (i.e. the worst-case time at which both robots meet at the target) measured as a function of the target distance from the origin (i.e. the time required to travel directly from the starting point to the target at unit velocity). We consider two standard models of communication between the robots, namely wireless communication and communication by meeting. In the case of communication by meeting, a robot learns about the target while sharing the same location with a robot possessing this knowledge. We propose here an optimal search strategy for two robots including the respective lower bound argument, for the full spectrum of their maximal speeds. This extends the main result of Chrobak et al. (in: Italiano, Margaria-Steffen, Pokorný, Quisquater, Wattenhofer (eds) Current trends in theory and practice of computer science, SOFSEM, 2015) referring to the exact complexity of the problem for the case when the speed of the slower robot is at least one third of the faster one. In the wireless communication model, a message sent by one robot is instantly received by the other robot, regardless of their current positions on the line. For this model, we design a strategy which is optimal whenever the faster robot is at most $$\sqrt{17}+4\approx 8.123$$ times faster than the slower one. We also prove that otherwise the wireless communication offers no advantage over communication by meeting.

We deal with the problem of maintaining a shortest-path tree rooted at some process r in a network that may be disconnected after topological changes. The goal is then to maintain a shortest-path tree rooted at r in its connected component, V\_r, and make all processes of other components detecting that r is not part of their connected component. We propose, in the composite atomicity model, a silent self-stabilizing algorithm for this problem working in semi-anonymous networks, where edges have strictly positive weights. This algorithm does not require any a priori knowledge about global parameters of the network. We prove its correctness assuming the distributed unfair daemon, the most general daemon. Its stabilization time in rounds is at most 3nmax+D, where nmax is the maximum number of non-root processes in a connected component and D is the hop-diameter of V\_r. Furthermore, if we additionally assume that edge weights are positive integers, then it stabilizes in a polynomial number of steps: namely, we exhibit a bound in O(maxi nmax^3 n), where maxi is the maximum weight of an edge and n is the number of processes.

This paper investigates compact routing schemes that are very efficient with respect to the memory used to store routing tables in internet-like graphs. We propose a new compact name-independent routing scheme whose theoretically proven average memory per node is upper-bounded by nγ, with constant γ < 1/2, while the maximum memory of any node is bounded by √n and the maximum stretch of any route is bounded by 5. These bounds are given for the Random Power Low Graphs (RPLG) and hold with high probability. Moreover, we experimentally show that our scheme is very efficient in terms of stretch and memory in internet-like graphs (CAIDA and other maps). We complete this study by comparing our analytic and experimental results to several compact routing schemes. In particular, we show that the average memory requirements is better by at least one order of magnitude than previous schemes for CAIDA maps on 16K nodes.

We study the problem of finding a destination node t by a mobile agent in an unreliable network having the structure of an unweighted graph, in a model first proposed by Hanusse et al [20, 21]. Each node of the network is able to give advice concerning the next node to visit so as to go closer to the target t. Unfortunately, exactly k of the nodes, called liars, give advice which is incorrect. It is known that for an n-node graph G of maximum degree Δ ≥ 3, reaching a target at a distance of d from the initial location may require an expected time of 2Ω(min d,k}), for any d,k = O(log n), even when G is a tree.

A finite automaton, simply referred to as a robot , has to explore a graph, that is, visit all the nodes of the graph. The robot has no a priori knowledge of the topology of the graph, nor of its size. It is known that for any k -state robot, there exists a graph of maximum degree 3 that the robot cannot explore. This article considers the effects of allowing the system designer to add short labels to the graph nodes in a preprocessing stage, for helping the exploration by the robot. We describe an exploration algorithm that, given appropriate 2-bit labels (in fact, only 3-valued labels), allows a robot to explore all graphs. Furthermore, we describe a suitable labeling algorithm for generating the required labels in linear time. We also show how to modify our labeling scheme so that a robot can explore all graphs of bounded degree, given appropriate 1-bit labels. In other words, although there is no robot able to explore all graphs of maximum degree 3, there is a robot R, and a way to color in black or white the nodes of any bounded-degree graph G , so that R can explore the colored graph G . Finally, we give impossibility results regarding graph exploration by a robot with no internal memory (i.e., a single-state automaton).

We study the problem of the amount of knowledge about a communication network that must be given to its nodes in order to efficiently disseminate information. While previous results about communication in networks used particular partial information available to nodes, such as the knowledge of the neighborhood or the knowledge of the network topology within some radius, our approach is quantitative: we investigate the minimum total number of bits of information (minimum oracle size) that has to be available to nodes in order to perform efficient communication.It turns out that the minimum oracle size for which a distributed task can be accomplished efficiently, can serve as a measure of the difficulty of this task. We use this measure to make a quantitative distinction between the difficulty of two apparently similar fundamental communication primitives: the broadcast and the wakeup. In both of them a distinguished node, called the source, has a message, which has to be transmitted to all other nodes of the network. In the wakeup, only nodes that already got the source message (i.e., are awake) can send messages to their neighbors, thus waking them up. In the broadcast, all nodes can send control messages even before getting the source message, thus potentially facilitating its future dissemination. In both cases we are interested in accomplishing the communication task with optimal message complexity, i.e., using a number of messages linear in the number of nodes.We show that the minimum oracle size permitting the wakeup with a linear number of messages in a n-node network, is Θ (n log n), while the broadcast with a linear number of messages can be achieved with an oracle of size O(n). We also show that the latter oracle size is almost optimal: no oracle of size o(n) can permit to broadcast with a linear number of messages. Thus an efficient wakeup requires strictly more information about the network than an efficient broadcast.