405 research outputs found
RoBuSt: A Crash-Failure-Resistant Distributed Storage System
In this work we present the first distributed storage system that is provably
robust against crash failures issued by an adaptive adversary, i.e., for each
batch of requests the adversary can decide based on the entire system state
which servers will be unavailable for that batch of requests. Despite up to
crashed servers, with constant and
denoting the number of servers, our system can correctly process any batch of
lookup and write requests (with at most a polylogarithmic number of requests
issued at each non-crashed server) in at most a polylogarithmic number of
communication rounds, with at most polylogarithmic time and work at each server
and only a logarithmic storage overhead.
Our system is based on previous work by Eikel and Scheideler (SPAA 2013), who
presented IRIS, a distributed information system that is provably robust
against the same kind of crash failures. However, IRIS is only able to serve
lookup requests. Handling both lookup and write requests has turned out to
require major changes in the design of IRIS.Comment: Revised full versio
Parallel Batch-Dynamic Graph Connectivity
In this paper, we study batch parallel algorithms for the dynamic
connectivity problem, a fundamental problem that has received considerable
attention in the sequential setting. The most well known sequential algorithm
for dynamic connectivity is the elegant level-set algorithm of Holm, de
Lichtenberg and Thorup (HDT), which achieves amortized time per
edge insertion or deletion, and time per query. We
design a parallel batch-dynamic connectivity algorithm that is work-efficient
with respect to the HDT algorithm for small batch sizes, and is asymptotically
faster when the average batch size is sufficiently large. Given a sequence of
batched updates, where is the average batch size of all deletions, our
algorithm achieves expected amortized work per
edge insertion and deletion and depth w.h.p. Our algorithm
answers a batch of connectivity queries in expected
work and depth w.h.p. To the best of our knowledge, our algorithm
is the first parallel batch-dynamic algorithm for connectivity.Comment: This is the full version of the paper appearing in the ACM Symposium
on Parallelism in Algorithms and Architectures (SPAA), 201
Adaptive Regret Minimization in Bounded-Memory Games
Online learning algorithms that minimize regret provide strong guarantees in
situations that involve repeatedly making decisions in an uncertain
environment, e.g. a driver deciding what route to drive to work every day.
While regret minimization has been extensively studied in repeated games, we
study regret minimization for a richer class of games called bounded memory
games. In each round of a two-player bounded memory-m game, both players
simultaneously play an action, observe an outcome and receive a reward. The
reward may depend on the last m outcomes as well as the actions of the players
in the current round. The standard notion of regret for repeated games is no
longer suitable because actions and rewards can depend on the history of play.
To account for this generality, we introduce the notion of k-adaptive regret,
which compares the reward obtained by playing actions prescribed by the
algorithm against a hypothetical k-adaptive adversary with the reward obtained
by the best expert in hindsight against the same adversary. Roughly, a
hypothetical k-adaptive adversary adapts her strategy to the defender's actions
exactly as the real adversary would within each window of k rounds. Our
definition is parametrized by a set of experts, which can include both fixed
and adaptive defender strategies.
We investigate the inherent complexity of and design algorithms for adaptive
regret minimization in bounded memory games of perfect and imperfect
information. We prove a hardness result showing that, with imperfect
information, any k-adaptive regret minimizing algorithm (with fixed strategies
as experts) must be inefficient unless NP=RP even when playing against an
oblivious adversary. In contrast, for bounded memory games of perfect and
imperfect information we present approximate 0-adaptive regret minimization
algorithms against an oblivious adversary running in time n^{O(1)}.Comment: Full Version. GameSec 2013 (Invited Paper
Fast and Compact Distributed Verification and Self-Stabilization of a DFS Tree
We present algorithms for distributed verification and silent-stabilization
of a DFS(Depth First Search) spanning tree of a connected network. Computing
and maintaining such a DFS tree is an important task, e.g., for constructing
efficient routing schemes. Our algorithm improves upon previous work in various
ways. Comparable previous work has space and time complexities of bits per node and respectively, where is the highest
degree of a node, is the number of nodes and is the diameter of the
network. In contrast, our algorithm has a space complexity of bits
per node, which is optimal for silent-stabilizing spanning trees and runs in
time. In addition, our solution is modular since it utilizes the
distributed verification algorithm as an independent subtask of the overall
solution. It is possible to use the verification algorithm as a stand alone
task or as a subtask in another algorithm. To demonstrate the simplicity of
constructing efficient DFS algorithms using the modular approach, We also
present a (non-sielnt) self-stabilizing DFS token circulation algorithm for
general networks based on our silent-stabilizing DFS tree. The complexities of
this token circulation algorithm are comparable to the known ones
Distributed Computing in the Asynchronous LOCAL model
The LOCAL model is among the main models for studying locality in the
framework of distributed network computing. This model is however subject to
pertinent criticisms, including the facts that all nodes wake up
simultaneously, perform in lock steps, and are failure-free. We show that
relaxing these hypotheses to some extent does not hurt local computing. In
particular, we show that, for any construction task associated to a locally
checkable labeling (LCL), if is solvable in rounds in the LOCAL model,
then remains solvable in rounds in the asynchronous LOCAL model.
This improves the result by Casta\~neda et al. [SSS 2016], which was restricted
to 3-coloring the rings. More generally, the main contribution of this paper is
to show that, perhaps surprisingly, asynchrony and failures in the computations
do not restrict the power of the LOCAL model, as long as the communications
remain synchronous and failure-free
Snap-Stabilization in Message-Passing Systems
In this paper, we tackle the open problem of snap-stabilization in
message-passing systems. Snap-stabilization is a nice approach to design
protocols that withstand transient faults. Compared to the well-known
self-stabilizing approach, snap-stabilization guarantees that the effect of
faults is contained immediately after faults cease to occur. Our contribution
is twofold: we show that (1) snap-stabilization is impossible for a wide class
of problems if we consider networks with finite yet unbounded channel capacity;
(2) snap-stabilization becomes possible in the same setting if we assume
bounded-capacity channels. We propose three snap-stabilizing protocols working
in fully-connected networks. Our work opens exciting new research perspectives,
as it enables the snap-stabilizing paradigm to be implemented in actual
networks
Local algorithms : Self-stabilization on speed
Non peer reviewe
Self-Stabilizing Byzantine Asynchronous Unison
We explore asynchronous unison in the presence of systemic transient and
permanent Byzantine faults in shared memory. We observe that the problem is not
solvable under less than strongly fair scheduler or for system topologies with
maximum node degree greater than two. We present a self-stabilizing
Byzantine-tolerant solution to asynchronous unison for chain and ring
topologies. Our algorithm has minimum possible containment radius and optimal
stabilization time
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