6,607 research outputs found
Computing on Anonymous Quantum Network
This paper considers distributed computing on an anonymous quantum network, a
network in which no party has a unique identifier and quantum communication and
computation are available. It is proved that the leader election problem can
exactly (i.e., without error in bounded time) be solved with at most the same
complexity up to a constant factor as that of exactly computing symmetric
functions (without intermediate measurements for a distributed and superposed
input), if the number of parties is given to every party. A corollary of this
result is a more efficient quantum leader election algorithm than existing
ones: the new quantum algorithm runs in O(n) rounds with bit complexity
O(mn^2), on an anonymous quantum network with n parties and m communication
links. Another corollary is the first quantum algorithm that exactly computes
any computable Boolean function with round complexity O(n) and with smaller bit
complexity than that of existing classical algorithms in the worst case over
all (computable) Boolean functions and network topologies. More generally, any
n-qubit state can be shared with that complexity on an anonymous quantum
network with n parties.Comment: 25 page
Memory lower bounds for deterministic self-stabilization
In the context of self-stabilization, a \emph{silent} algorithm guarantees
that the register of every node does not change once the algorithm has
stabilized. At the end of the 90's, Dolev et al. [Acta Inf. '99] showed that,
for finding the centers of a graph, for electing a leader, or for constructing
a spanning tree, every silent algorithm must use a memory of
bits per register in -node networks. Similarly, Korman et al. [Dist. Comp.
'07] proved, using the notion of proof-labeling-scheme, that, for constructing
a minimum-weight spanning trees (MST), every silent algorithm must use a memory
of bits per register. It follows that requiring the algorithm
to be silent has a cost in terms of memory space, while, in the context of
self-stabilization, where every node constantly checks the states of its
neighbors, the silence property can be of limited practical interest. In fact,
it is known that relaxing this requirement results in algorithms with smaller
space-complexity.
In this paper, we are aiming at measuring how much gain in terms of memory
can be expected by using arbitrary self-stabilizing algorithms, not necessarily
silent. To our knowledge, the only known lower bound on the memory requirement
for general algorithms, also established at the end of the 90's, is due to
Beauquier et al.~[PODC '99] who proved that registers of constant size are not
sufficient for leader election algorithms. We improve this result by
establishing a tight lower bound of bits per
register for self-stabilizing algorithms solving -coloring or
constructing a spanning tree in networks of maximum degree~. The lower
bound bits per register also holds for leader election
Time vs. Information Tradeoffs for Leader Election in Anonymous Trees
The leader election task calls for all nodes of a network to agree on a
single node. If the nodes of the network are anonymous, the task of leader
election is formulated as follows: every node of the network must output a
simple path, coded as a sequence of port numbers, such that all these paths end
at a common node, the leader. In this paper, we study deterministic leader
election in anonymous trees.
Our aim is to establish tradeoffs between the allocated time and the
amount of information that has to be given to the nodes to
enable leader election in time in all trees for which leader election in
this time is at all possible. Following the framework of , this information (a single binary string) is provided to all
nodes at the start by an oracle knowing the entire tree. The length of this
string is called the . For an allocated time ,
we give upper and lower bounds on the minimum size of advice sufficient to
perform leader election in time .
We consider -node trees of diameter . While leader election
in time can be performed without any advice, for time we give
tight upper and lower bounds of . For time we give
tight upper and lower bounds of for even values of ,
and tight upper and lower bounds of for odd values of .
For the time interval for constant ,
we prove an upper bound of and a lower bound of
, the latter being valid whenever is odd or when
the time is at most . Finally, for time for any
constant (except for the case of very small diameters), we give
tight upper and lower bounds of
Deterministic Symmetry Breaking in Ring Networks
We study a distributed coordination mechanism for uniform agents located on a
circle. The agents perform their actions in synchronised rounds. At the
beginning of each round an agent chooses the direction of its movement from
clockwise, anticlockwise, or idle, and moves at unit speed during this round.
Agents are not allowed to overpass, i.e., when an agent collides with another
it instantly starts moving with the same speed in the opposite direction
(without exchanging any information with the other agent). However, at the end
of each round each agent has access to limited information regarding its
trajectory of movement during this round.
We assume that mobile agents are initially located on a circle unit
circumference at arbitrary but distinct positions unknown to other agents. The
agents are equipped with unique identifiers from a fixed range. The {\em
location discovery} task to be performed by each agent is to determine the
initial position of every other agent.
Our main result states that, if the only available information about movement
in a round is limited to %information about distance between the initial and
the final position, then there is a superlinear lower bound on time needed to
solve the location discovery problem. Interestingly, this result corresponds to
a combinatorial symmetry breaking problem, which might be of independent
interest. If, on the other hand, an agent has access to the distance to its
first collision with another agent in a round, we design an asymptotically
efficient and close to optimal solution for the location discovery problem.Comment: Conference version accepted to ICDCS 201
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