39 research outputs found
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 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 , after (1+\e)(\frac{1}{\log_2(1+p)}\log_2n+\frac{1}{p}\ln n)
rounds the quasirandom protocol has informed all nodes in the network with
probability at least 1-n^{-p\e/40}. Note that this is faster than the
intuitively natural factor increase over the run-time of approximately
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
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 rounds in a network
of diameter , withno dependence on the conductance. This is at most an
additive polylogarithmic factor from the trivial lower bound of , which
applies even in the LOCAL model. In fact, we prove that something stronger is
true: any algorithm that requires rounds in the LOCAL model can be
simulated in rounds in the GOSSIP model. We thus
prove that these two models of distributed computation are essentially
equivalent
Quasirandom Rumor Spreading
We propose and analyze a quasirandom analogue of the classical push model for disseminating information in networks (ârandomized rumor spreadingâ). 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, ErdĆs-RĂ©nyi random graphs, and Ramanujan graphs with probability 1 â
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.
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Gossip vs. Markov Chains, and Randomness-Efficient Rumor Spreading
We study gossip algorithms for the rumor spreading problem which asks one
node to deliver a rumor to all nodes in an unknown network. We present the
first protocol for any expander graph with nodes such that, the
protocol informs every node in rounds with high probability, and
uses random bits in total. The runtime of our protocol is
tight, and the randomness requirement of random bits almost
matches the lower bound of random bits for dense graphs. We
further show that, for many graph families, polylogarithmic number of random
bits in total suffice to spread the rumor in rounds.
These results together give us an almost complete understanding of the
randomness requirement of this fundamental gossip process.
Our analysis relies on unexpectedly tight connections among gossip processes,
Markov chains, and branching programs. First, we establish a connection between
rumor spreading processes and Markov chains, which is used to approximate the
rumor spreading time by the mixing time of Markov chains. Second, we show a
reduction from rumor spreading processes to branching programs, and this
reduction provides a general framework to derandomize gossip processes. In
addition to designing rumor spreading protocols, these novel techniques may
have applications in studying parallel and multiple random walks, and
randomness complexity of distributed algorithms.Comment: 41 pages, 1 figure. arXiv admin note: substantial text overlap with
arXiv:1304.135
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, nĂ€mlich randomisierte Rundungen und randomisierte Algorithmen zur GerĂŒchteverbreitung. Der Satz von Beck und Fiala (1981) sagt aus, dass es fĂŒr jeden Hypergraphen und fĂŒr jeden Satz von Knotengewichten eine Rundung gibt derart, dass der Rundungsfehler pro Kante vom Maximalgrad beschrĂ€nkt wird. Im ersten Teil der Arbeit wird dieser Satz auf den Fall randomisierter Rundungen verallgemeinert, das heiĂt auf zufĂ€llige Rundungen, bei denen jede Zahl mit der Wahrscheinlichkeit entsprechend ihren Nachkommastellen aufgerundet wird. Der zweite, gröĂere Teil der Arbeit handelt von randomisierten Algorithmen zur GerĂŒchteverbreitung. Das klassische "Randomized Rumor Spreading" wurde von Frieze und Grimmett (1985) eingefĂŒhrt. Ihre Ergebnisse werden in Kapitel 3 sowohl hinsichtlich des Modells als auch hinsichtlich des zugrundegelegten Graphen verallgemeinert. In Kapitel 4 wird ein quasizufĂ€lliges Modell zur GerĂŒchteverbreitung betrachtet und gezeigt, dass es bezĂŒglich Laufzeit und Robustheit dem klassischen Modell gleichwertig ist. Gemeinsam liegt beiden Teilen der Arbeit die Idee zugrunde, stochastische AbhĂ€ngigkeiten zu nutzen um Ergebnisse zu erzielen, die den unter Verwendung stochastischer UnabhĂ€ngigkeit erzielten gleichwertig oder ĂŒberlegen sind
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]