Quasirandom Rumor Spreading

We propose and analyze a quasirandom analogue of the classical push model for disseminating information in networks (“randomized rumor spreading”). In the classical model, in each round, each informed vertex chooses a neighbor at random and informs it, if it was not informed before. It is known that this simple protocol succeeds in spreading a rumor from one vertex to all others within O (log n ) rounds on complete graphs, hypercubes, random regular graphs, Erdős-Rényi random graphs, and Ramanujan graphs with probability 1 − o (1). In the quasirandom model, we assume that each vertex has a (cyclic) list of its neighbors. Once informed, it starts at a random position on the list, but from then on informs its neighbors in the order of the list. Surprisingly, irrespective of the orders of the lists, the above-mentioned bounds still hold. In some cases, even better bounds than for the classical model can be shown. </jats:p

Quasirandom Rumor Spreading: An Experimental Analysis

We empirically analyze two versions of the well-known "randomized rumor spreading" protocol to disseminate a piece of information in networks. In the classical model, in each round each informed node informs a random neighbor. In the recently proposed quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at a random position of the list, but from then on informs its neighbors in the order of the list. While for sparse random graphs a better performance of the quasirandom model could be proven, all other results show that, independent of the structure of the lists, the same asymptotic performance guarantees hold as for the classical model. In this work, we compare the two models experimentally. This not only shows that the quasirandom model generally is faster, but also that the runtime is more concentrated around the mean. This is surprising given that much fewer random bits are used in the quasirandom process. These advantages are also observed in a lossy communication model, where each transmission does not reach its target with a certain probability, and in an asynchronous model, where nodes send at random times drawn from an exponential distribution. We also show that typically the particular structure of the lists has little influence on the efficiency.Comment: 14 pages, appeared in ALENEX'0

Strong Robustness of Randomized Rumor Spreading Protocols

Randomized rumor spreading is a classical protocol to disseminate information across a network. At SODA 2008, a quasirandom version of this protocol was proposed and competitive bounds for its run-time were proven. This prompts the question: to what extent does the quasirandom protocol inherit the second principal advantage of randomized rumor spreading, namely robustness against transmission failures? In this paper, we present a result precise up to $(1 \pm o(1))$ factors. We limit ourselves to the network in which every two vertices are connected by a direct link. Run-times accurate to their leading constants are unknown for all other non-trivial networks. We show that if each transmission reaches its destination with a probability of $p \in (0,1]$, after (1+\e)(\frac{1}{\log_2(1+p)}\log_2n+\frac{1}{p}\ln n) rounds the quasirandom protocol has informed all $n$ nodes in the network with probability at least 1-n^{-p\e/40}. Note that this is faster than the intuitively natural $1/p$ factor increase over the run-time of approximately $\log_2 n + \ln n$ for the non-corrupted case. We also provide a corresponding lower bound for the classical model. This demonstrates that the quasirandom model is at least as robust as the fully random model despite the greatly reduced degree of independent randomness.Comment: Accepted for publication in "Discrete Applied Mathematics". A short version appeared in the proceedings of the 20th International Symposium on Algorithms and Computation (ISAAC 2009). Minor typos fixed in the second version. Proofs of Lemma 11 and Theorem 12 fixed in the third version. Proof of Lemma 8 fixed in the fourth versio

Communication Complexity of Quasirandom Rumor Spreading

We consider rumor spreading on random graphs and hypercubes in the quasirandom phone call model. In this model, every node has a list of neighbors whose order is specified by an adversary. In step i every node opens a channel to its ith neighbor (modulo degree) on that list, beginning from a randomly chosen starting position. Then, the channels can be used for bi-directional communication in that step. The goal is to spread a message efficiently to all nodes of the graph.For random graphs (with sufficiently many edges) we present an address-oblivious algorithm with runtime O(logn) that uses at most O(nloglogn) message transmissions. For hypercubes of dimension logn we present an address-oblivious algorithm with runtime O(logn) that uses at most O(n(loglogn)2) message transmissions.Together with a result of Elsässer (Proc. of SPAA’06, pp. 148–157, 2006), our results imply that for random graphs the communication complexity of the quasirandom phone call model is significantly smaller than that of the standard phone call model

Quasi-Random Rumor Spreading: Reducing Randomness Can Be Costly

We give a time-randomness tradeoff for the quasi-random rumor spreading protocol proposed by Doerr, Friedrich and Sauerwald [SODA 2008] on complete graphs. In this protocol, the goal is to spread a piece of information originating from one vertex throughout the network. Each vertex is assumed to have a (cyclic) list of its neighbors. Once a vertex is informed by one of its neighbors, it chooses a position in its list uniformly at random and then informs its neighbors starting from that position and proceeding in order of the list. Angelopoulos, Doerr, Huber and Panagiotou [Electron.~J.~Combin.~2009] showed that after $(1+o(1))(\log_2 n + \ln n)$ rounds, the rumor will have been broadcasted to all nodes with probability $1 - o(1)$. We study the broadcast time when the amount of randomness available at each node is reduced in natural way. In particular, we prove that if each node can only make its initial random selection from every $\ell$-th node on its list, then there exists lists such that $(1-\varepsilon) (\log_2 n + \ln n - \log_2 \ell - \ln \ell)+\ell-1$ steps are needed to inform every vertex with probability at least $1-O\bigl(\exp\bigl(-\frac{n^\varepsilon}{2\ln n}\bigr)\bigr)$. This shows that a further reduction of the amount of randomness used in a simple quasi-random protocol comes at a loss of efficiency

Randomized rounding and rumor spreading with stochastic dependencies

Randomness is an important ingredient of modern computer science. The present thesis is concerned with two uses of randomness, viz. randomized roundings and randomized rumor spreading algorithms. The theorem of Beck and Fiala (1981) asserts that for every hypergraph and every set of vertex weights there is a rounding of the vertex weights such that the additive rounding error for all hyperedges is bounded by the maximum degree. In Chapter 2 this theorem will be extended to randomized roundings, that is, to roundings that are efficiently generated at random in such a way that each value is rounded up with probability equal to its fractional part. The larger part of this thesis deals with randomized rumor spreading algorithms. These are protocols for disseminating information on graphs. The classical randomized rumor spreading was introduced and first investigated by Frieze and Grimmett on the complete graph (1985). In Chapter 3 a generalization of their results both in terms of the model used and in terms of the underlying graph will be shown. In Chapter 4 a quasirandom rumor spreading protocol introduced by Doerr, Friedrich, and Sauerwald (2008) will be considered. We present a detailed analysis of its evolution and show that its performance and robustness match performance and robustness of the randomized rumor spreading protocol. The unifying idea is to use dependencies so as to obtain results that are superior or equal to those obtained via independent randomness.Die Verwendung von Zufallselementen ist ein wichtiger Bestandteil der modernen Informatik. Die vorliegende Arbeit untersucht zwei Bereiche, in denen randomisierte Methoden Verwendung finden, nämlich randomisierte Rundungen und randomisierte Algorithmen zur Gerüchteverbreitung. Der Satz von Beck und Fiala (1981) sagt aus, dass es für jeden Hypergraphen und für jeden Satz von Knotengewichten eine Rundung gibt derart, dass der Rundungsfehler pro Kante vom Maximalgrad beschränkt wird. Im ersten Teil der Arbeit wird dieser Satz auf den Fall randomisierter Rundungen verallgemeinert, das heißt auf zufällige Rundungen, bei denen jede Zahl mit der Wahrscheinlichkeit entsprechend ihren Nachkommastellen aufgerundet wird. Der zweite, größere Teil der Arbeit handelt von randomisierten Algorithmen zur Gerüchteverbreitung. Das klassische "Randomized Rumor Spreading" wurde von Frieze und Grimmett (1985) eingeführt. Ihre Ergebnisse werden in Kapitel 3 sowohl hinsichtlich des Modells als auch hinsichtlich des zugrundegelegten Graphen verallgemeinert. In Kapitel 4 wird ein quasizufälliges Modell zur Gerüchteverbreitung betrachtet und gezeigt, dass es bezüglich Laufzeit und Robustheit dem klassischen Modell gleichwertig ist. Gemeinsam liegt beiden Teilen der Arbeit die Idee zugrunde, stochastische Abhängigkeiten zu nutzen um Ergebnisse zu erzielen, die den unter Verwendung stochastischer Unabhängigkeit erzielten gleichwertig oder überlegen sind

Global Computation in a Poorly Connected World: Fast Rumor Spreading with No Dependence on Conductance

In this paper, we study the question of how efficiently a collection of interconnected nodes can perform a global computation in the widely studied GOSSIP model of communication. In this model, nodes do not know the global topology of the network, and they may only initiate contact with a single neighbor in each round. This model contrasts with the much less restrictive LOCAL model, where a node may simultaneously communicate with all of its neighbors in a single round. A basic question in this setting is how many rounds of communication are required for the information dissemination problem, in which each node has some piece of information and is required to collect all others. In this paper, we give an algorithm that solves the information dissemination problem in at most $O(D+\text{polylog}{(n)})$ rounds in a network of diameter $D$, withno dependence on the conductance. This is at most an additive polylogarithmic factor from the trivial lower bound of $D$, which applies even in the LOCAL model. In fact, we prove that something stronger is true: any algorithm that requires $T$ rounds in the LOCAL model can be simulated in $O(T +\mathrm{polylog}(n))$ rounds in the GOSSIP model. We thus prove that these two models of distributed computation are essentially equivalent

Bit-complexité des protocoles de gossip

National audienceNous étudions le problème du \emph{gossip} (i.e., diffusion de rumeurs) dans le modèle des appels aléatoires. Considérons $n$ noeuds communiquant en parallèle par étape. A chaque étape, un ensemble (potentiellement vide) de \emph{rumeurs} est généré à chaque noeud, la même rumeur pouvant être générée simultanément par plusieurs noeuds. L'objectif est de diffuser ces rumeurs à tous les noeuds. Pour ce faire, à chaque étape, chaque noeud appelle un autre noeud choisi uniformément aléatoirement parmi l'ensemble de tous les noeuds, et un noeud ne peut alors communiquer qu'avec le noeud qu'il a appelé, et les noeuds qui l'ont potentiellement appelé. Dans ce modèle, Karp et ses co-auteurs~\cite{Karp2000} ont montré qu'aucun algorithme de gossip ne peut être à la fois optimal en temps (i.e., s'exécuter en $O(\log n)$ étapes) et en volume de communication (i.e., s'exécuter en transmettant au plus $O(n)$ messages). En particulier, ils ont montré que tout algorithme de gossip n'utilisant pas les IDs des noeuds et diffusant toute rumeur en $O(\log n)$ étapes doit échanger $\Omega(n\log\log n)$ messages par rumeur. Karp et ses co-auteurs ont également montré que ce compromis peut être atteint. Dans cet article, nous étudions le volume de communication estimé en nombre de bits échangés plutôt qu'en nombre de messages. Nous montrons tout d'abord que tout algorithme de gossip n'utilisant pas les IDs des noeuds et diffusant toute rumeur en $O(\log n)$ étapes doit échanger $\Omega(n (b+\log\log n))$ bits pour diffuser une rumeur de $b$ bits. Nous proposons alors un algorithme de gossip n'utilisant pas les IDs des noeuds qui diffuse toute rumeur en $O(\log n)$ étapes, en échangeant $O(n(b+\log\log n\log b))$ bits pour une rumeur de $b$ bits. Ces résultats démontrent que contrairement à ce qu'il peut sembler lorsque l'on mesure le volume de communication en nombre de messages, il est possible d'être à la fois optimal en temps (i.e., s'exécuter en $O(\log n)$ étapes) et en volume de communication (i.e., s'exécuter en transmettant au plus $O(nb)$ bits), sauf pour des rumeurs extrêmement petites, de taille $b\ll\log\log n \log\log\log n$ bits

Low Randomness Rumor Spreading via Hashing

International audienceWe consider the classical rumor spreading problem, where a piece of information must be disseminated from a single node to all n nodes of a given network. We devise two simple push-based protocols, in which nodes choose the neighbor they send the information to in each round using pairwise independent hash functions, or a pseudo-random generator, respectively. For several well-studied topologies our algorithms use exponentially fewer random bits than previous protocols. For example, in complete graphs, expanders, and random graphs only a polylogarithmic number of random bits are needed in total to spread the rumor in O(log n) rounds with high probability. Previous explicit algorithms require Omega(n) random bits to achieve the same round complexity. For complete graphs, the amount of randomness used by our hashing-based algorithm is within an O(log n)-factor of the theoretical minimum determined by [Giakkoupis and Woelfel, 2011]