41,197 research outputs found
Fast Discrete Consensus Based on Gossip for Makespan Minimization in Networked Systems
In this paper we propose a novel algorithm to solve the discrete consensus problem, i.e., the problem of distributing evenly a set of tokens of arbitrary weight among the nodes of a networked system. Tokens are tasks to be executed by the nodes and the proposed distributed algorithm minimizes monotonically the makespan of the assigned tasks. The algorithm is based on gossip-like asynchronous local interactions between the nodes. The convergence time of the proposed algorithm is superior with respect to the state of the art of discrete and quantized consensus by at least a factor O(n) in both theoretical and empirical comparisons
Locally Optimal Load Balancing
This work studies distributed algorithms for locally optimal load-balancing:
We are given a graph of maximum degree , and each node has up to
units of load. The task is to distribute the load more evenly so that the loads
of adjacent nodes differ by at most .
If the graph is a path (), it is easy to solve the fractional
version of the problem in communication rounds, independently of the
number of nodes. We show that this is tight, and we show that it is possible to
solve also the discrete version of the problem in rounds in paths.
For the general case (), we show that fractional load balancing
can be solved in rounds and discrete load
balancing in rounds for some function , independently of the
number of nodes.Comment: 19 pages, 11 figure
Quasirandom Load Balancing
We propose a simple distributed algorithm for balancing indivisible tokens on
graphs. The algorithm is completely deterministic, though it tries to imitate
(and enhance) a random algorithm by keeping the accumulated rounding errors as
small as possible.
Our new algorithm surprisingly closely approximates the idealized process
(where the tokens are divisible) on important network topologies. On
d-dimensional torus graphs with n nodes it deviates from the idealized process
only by an additive constant. In contrast to that, the randomized rounding
approach of Friedrich and Sauerwald (2009) can deviate up to Omega(polylog(n))
and the deterministic algorithm of Rabani, Sinclair and Wanka (1998) has a
deviation of Omega(n^{1/d}). This makes our quasirandom algorithm the first
known algorithm for this setting which is optimal both in time and achieved
smoothness. We further show that also on the hypercube our algorithm has a
smaller deviation from the idealized process than the previous algorithms.Comment: 25 page
Improved Analysis of Deterministic Load-Balancing Schemes
We consider the problem of deterministic load balancing of tokens in the
discrete model. A set of processors is connected into a -regular
undirected network. In every time step, each processor exchanges some of its
tokens with each of its neighbors in the network. The goal is to minimize the
discrepancy between the number of tokens on the most-loaded and the
least-loaded processor as quickly as possible.
Rabani et al. (1998) present a general technique for the analysis of a wide
class of discrete load balancing algorithms. Their approach is to characterize
the deviation between the actual loads of a discrete balancing algorithm with
the distribution generated by a related Markov chain. The Markov chain can also
be regarded as the underlying model of a continuous diffusion algorithm. Rabani
et al. showed that after time , any algorithm of their
class achieves a discrepancy of , where is the spectral
gap of the transition matrix of the graph, and is the initial load
discrepancy in the system.
In this work we identify some natural additional conditions on deterministic
balancing algorithms, resulting in a class of algorithms reaching a smaller
discrepancy. This class contains well-known algorithms, eg., the Rotor-Router.
Specifically, we introduce the notion of cumulatively fair load-balancing
algorithms where in any interval of consecutive time steps, the total number of
tokens sent out over an edge by a node is the same (up to constants) for all
adjacent edges. We prove that algorithms which are cumulatively fair and where
every node retains a sufficient part of its load in each step, achieve a
discrepancy of in time . We
also show that in general neither of these assumptions may be omitted without
increasing discrepancy. We then show by a combinatorial potential reduction
argument that any cumulatively fair scheme satisfying some additional
assumptions achieves a discrepancy of almost as quickly as the
continuous diffusion process. This positive result applies to some of the
simplest and most natural discrete load balancing schemes.Comment: minor corrections; updated literature overvie
Tight Bounds for Randomized Load Balancing on Arbitrary Network Topologies
We consider the problem of balancing load items (tokens) in networks.
Starting with an arbitrary load distribution, we allow nodes to exchange tokens
with their neighbors in each round. The goal is to achieve a distribution where
all nodes have nearly the same number of tokens.
For the continuous case where tokens are arbitrarily divisible, most load
balancing schemes correspond to Markov chains, whose convergence is fairly
well-understood in terms of their spectral gap. However, in many applications,
load items cannot be divided arbitrarily, and we need to deal with the discrete
case where the load is composed of indivisible tokens. This discretization
entails a non-linear behavior due to its rounding errors, which makes this
analysis much harder than in the continuous case.
We investigate several randomized protocols for different communication
models in the discrete case. As our main result, we prove that for any regular
network in the matching model, all nodes have the same load up to an additive
constant in (asymptotically) the same number of rounds as required in the
continuous case. This generalizes and tightens the previous best result, which
only holds for expander graphs, and demonstrates that there is almost no
difference between the discrete and continuous cases. Our results also provide
a positive answer to the question of how well discrete load balancing can be
approximated by (continuous) Markov chains, which has been posed by many
researchers.Comment: 74 pages, 4 figure
Balancing sums of random vectors
We study a higher-dimensional 'balls-into-bins' problem. An infinite sequence
of i.i.d. random vectors is revealed to us one vector at a time, and we are
required to partition these vectors into a fixed number of bins in such a way
as to keep the sums of the vectors in the different bins close together; how
close can we keep these sums almost surely? This question, our primary focus in
this paper, is closely related to the classical problem of partitioning a
sequence of vectors into balanced subsequences, in addition to having
applications to some problems in computer science.Comment: 17 pages, Discrete Analysi
Lock-in Problem for Parallel Rotor-router Walks
The rotor-router model, also called the Propp machine, was introduced as a
deterministic alternative to the random walk. In this model, a group of
identical tokens are initially placed at nodes of the graph. Each node
maintains a cyclic ordering of the outgoing arcs, and during consecutive turns
the tokens are propagated along arcs chosen according to this ordering in
round-robin fashion. The behavior of the model is fully deterministic. Yanovski
et al.(2003) proved that a single rotor-router walk on any graph with m edges
and diameter stabilizes to a traversal of an Eulerian circuit on the set of
all 2m directed arcs on the edge set of the graph, and that such periodic
behaviour of the system is achieved after an initial transient phase of at most
2mD steps. The case of multiple parallel rotor-routers was studied
experimentally, leading Yanovski et al. to the conjecture that a system of k
\textgreater{} 1 parallel walks also stabilizes with a period of length at
most steps. In this work we disprove this conjecture, showing that the
period of parallel rotor-router walks can in fact, be superpolynomial in the
size of graph. On the positive side, we provide a characterization of the
periodic behavior of parallel router walks, in terms of a structural property
of stable states called a subcycle decomposition. This property provides us the
tools to efficiently detect whether a given system configuration corresponds to
the transient or to the limit behavior of the system. Moreover, we provide
polynomial upper bounds of and on the
number of steps it takes for the system to stabilize. Thus, we are able to
predict any future behavior of the system using an algorithm that takes
polynomial time and space. In addition, we show that there exists a separation
between the stabilization time of the single-walk and multiple-walk
rotor-router systems, and that for some graphs the latter can be asymptotically
larger even for the case of walks
- …