19 research outputs found
Bounds for self-stabilization in unidirectional networks
A distributed algorithm is self-stabilizing if after faults and attacks hit
the system and place it in some arbitrary global state, the systems recovers
from this catastrophic situation without external intervention in finite time.
Unidirectional networks preclude many common techniques in self-stabilization
from being used, such as preserving local predicates. In this paper, we
investigate the intrinsic complexity of achieving self-stabilization in
unidirectional networks, and focus on the classical vertex coloring problem.
When deterministic solutions are considered, we prove a lower bound of
states per process (where is the network size) and a recovery time of at
least actions in total. We present a deterministic algorithm with
matching upper bounds that performs in arbitrary graphs. When probabilistic
solutions are considered, we observe that at least states per
process and a recovery time of actions in total are required (where
denotes the maximal degree of the underlying simple undirected graph).
We present a probabilistically self-stabilizing algorithm that uses
states per process, where is a parameter of the
algorithm. When , the algorithm recovers in expected
actions. When may grow arbitrarily, the algorithm
recovers in expected O(n) actions in total. Thus, our algorithm can be made
optimal with respect to space or time complexity
Stabilizing data-link over non-FIFO channels with optimal fault-resilience
Self-stabilizing systems have the ability to converge to a correct behavior
when started in any configuration. Most of the work done so far in the
self-stabilization area assumed either communication via shared memory or via
FIFO channels. This paper is the first to lay the bases for the design of
self-stabilizing message passing algorithms over unreliable non-FIFO channels.
We propose a fault-send-deliver optimal stabilizing data-link layer that
emulates a reliable FIFO communication channel over unreliable capacity bounded
non-FIFO channels
Silent MST approximation for tiny memory
In network distributed computing, minimum spanning tree (MST) is one of the
key problems, and silent self-stabilization one of the most demanding
fault-tolerance properties. For this problem and this model, a polynomial-time
algorithm with memory is known for the state model. This is
memory optimal for weights in the classic range (where
is the size of the network). In this paper, we go below this
memory, using approximation and parametrized complexity.
More specifically, our contributions are two-fold. We introduce a second
parameter~, which is the space needed to encode a weight, and we design a
silent polynomial-time self-stabilizing algorithm, with space . In turn, this allows us to get an approximation algorithm for the problem,
with a trade-off between the approximation ratio of the solution and the space
used. For polynomial weights, this trade-off goes smoothly from memory for an -approximation, to memory for exact solutions,
with for example memory for a 2-approximation
Communication Efficiency in Self-stabilizing Silent Protocols
Self-stabilization is a general paradigm to provide forward recovery
capabilities to distributed systems and networks. Intuitively, a protocol is
self-stabilizing if it is able to recover without external intervention from
any catastrophic transient failure. In this paper, our focus is to lower the
communication complexity of self-stabilizing protocols \emph{below} the need of
checking every neighbor forever. In more details, the contribution of the paper
is threefold: (i) We provide new complexity measures for communication
efficiency of self-stabilizing protocols, especially in the stabilized phase or
when there are no faults, (ii) On the negative side, we show that for
non-trivial problems such as coloring, maximal matching, and maximal
independent set, it is impossible to get (deterministic or probabilistic)
self-stabilizing solutions where every participant communicates with less than
every neighbor in the stabilized phase, and (iii) On the positive side, we
present protocols for coloring, maximal matching, and maximal independent set
such that a fraction of the participants communicates with exactly one neighbor
in the stabilized phase
On the computational power of self-stabilizing systems
AbstractThe computational power of self-stabilizing distributed systems is examined. Assuming availability of any number of processors, each with (small) constant size memory we show that any computable problem can be realized in a self-stabilizing fashion.The result is derived by presenting a distributed system which tolerates transient faults and simulates the execution of a Turing machine. The total amount of memory required by the distributed system is equal to the memory used by the Turing machine (up to a constant factor)
Compact Deterministic Self-Stabilizing Leader Election: The Exponential Advantage of Being Talkative
This paper focuses on compact deterministic self-stabilizing solutions for
the leader election problem. When the protocol is required to be \emph{silent}
(i.e., when communication content remains fixed from some point in time during
any execution), there exists a lower bound of Omega(\log n) bits of memory per
node participating to the leader election (where n denotes the number of nodes
in the system). This lower bound holds even in rings. We present a new
deterministic (non-silent) self-stabilizing protocol for n-node rings that uses
only O(\log\log n) memory bits per node, and stabilizes in O(n\log^2 n) rounds.
Our protocol has several attractive features that make it suitable for
practical purposes. First, the communication model fits with the model used by
existing compilers for real networks. Second, the size of the ring (or any
upper bound on this size) needs not to be known by any node. Third, the node
identifiers can be of various sizes. Finally, no synchrony assumption, besides
a weakly fair scheduler, is assumed. Therefore, our result shows that, perhaps
surprisingly, trading silence for exponential improvement in term of memory
space does not come at a high cost regarding stabilization time or minimal
assumptions
Memory requirements for silent stabilization
A self-stabilizing algorithm is silent if it converges to a glc)bal state after which the values stored in the com-munication registers are fixed. The silence property of self-stabilizing algorithms is a desirable property in terms of simplicity and communication overhead. In this work we show that no constant memory silent self-stabilizing algorithms exist for identification of the centers of a graph, leader election, and spanning tree construction. Lower bounds of Cl(log n) bits per communication register are obtained for each of the above tasks. The existence of a silent legitimate global state that uses less than log n bits per register is assumed. This legitimate global state is used to construct a silent global state that is illegitimate.