17834776
Self-stabilizing algorithms for finding centers and medians of trees
Locating a center or a median in a graph is a fundamental graph-theoretic problem. Centers and medians are especially important in distributed systems because they are ideal locations for placing resources that need to be shared among dierent processes in a network. This paper presents simple self-stabilizing algorithms for locating centers and medians of trees. Since these algorithms are self-stabilizing, they can tolerate transient failures. In addition, they can automatically adjust to a dynamically changing tree topology. After the algorithms are presented, their correctness is proven and upper bounds on their time complexity are established. Finally, extensions of our algorithms to trees with arbitrary, positive edge costs are sketched.
1421735
Scalable Self-Stabilization
This paper presents a methodology for a synchronous non-reactive distributed system on a tree topology to stabilize from a k-faulty configuration in a time independent of the size n of the system. In the proposed methodology, processes first measure and compare the sizes of the faulty regions, and then use this information to schedule actions in such a way that the size of the faulty regions progressively shrink, until they completely disappear. We demonstrate that when k processes fail, the stabilization time is O(k2). Apart from its applicability to a wide class of problems, the proposed method achieves scalability with a low space complexity of O(�.(�.k+log2n)) per process, where � is the maximum degree of a node.
9120365
A self-stabilizing algorithm for the maximum flow problem
Summary. The maximum flow problem is a fundamental problem in graph theory and combinatorial optimization with a variety of important applications. Known distributed algorithms for this problem do not tolerate faults or adjust to dynamic changes in network topology. This paper presents a distributed self-stabilizing algorithm for the maximum flow problem. Starting from an arbitrary state, the algorithm computes the maximum flow in an acyclic network in finitely many steps. Since the algorithm is self-stabilizing, it is inherently tolerant to transient faults. It can automatically adjust to topology changes and to changes in other parameters of the problem. The paper presents results obtained by extensively experimenting with the algorithm. Two main observations based on these results are (1) the algorithm requires fewer than n2 moves for almost all test cases and (2) the algorithm consistently performs at least as well as a distributed implementation of the well-known Goldberg-Tarjan algorithm for almost all test cases. The paper ends with the conjecture that the algorithm correctly computes a maximum flow even in networks that contain cycles.
11125013
A self-stabilizing algorithm for coloring planar graphs
SummaryThis paper describes an algorithm for coloring the nodes of a planar graph with no more than six colors using a self-stabilizing approach. The first part illustrates the coloring algorithm on a directed acyclic version of the given planar graph. The second part describes a selfstabilizing algorithm for generating the directed acyclic version of the planar graph, and combines the two algorithms into one.
34174199
Selfish Stabilization
31511581
Mutual Exclusion on a Hypercube
Abstract We present a decentralized, symmetric mutual exclusion algorithm that is tailored to the hypercube architecture. Our algorithm has better time complexity than a suitable hypercube adaptation of Maekawa′s O(√N) Mutual Exclusion algorithm. We show that, in the presence of contention, our algorithm has a shorter Blocking Delay than Maekawa′s algorithm. In the absence of contention, our algorithm achieves optimal Round-trip Delay under both the n-port and the one-port communication models.
972043
Stochastic Modeling of Cytoplasmic Reactions in Complex Biological Systems
The use of “in silico” stochastic event based modeling can identify the dynamic interactions of different processes in a complex biological system. This requires the computation of the time taken by different events in the system based on their biological functions. One such important event is the reactions between the molecules inside the cytoplasm of a cell. We present a mathematical formulation for the estimation of the reaction time between two molecules within a cell based on the system state. We derive expressions for the average and second moment of the time for reaction to be used by our stochastic event-based simulation. Unlike rate equations, the proposed model does not require the assumption of concentration stability for multiple molecule reactions.
15460911
Polyhedral sampling for multiattribute preference elicitation
A basic requirement for running multiattribute auctions is knowledge of the utility function of the buyer that trades off nonprice attributes against price. We present and study an approach that elicits this preference structure based on a markovian polyhedral sampling scheme called the "Hit-and-Run" algorithm. An advantage of this technique is its relative simplicity - it relies only on matrix algebra as opposed to the use of nonlinear optimization techniques by other methods in the literature. Computational results suggest that this method is fast and accurate.
1136530
A graph-theoretic approach for studying the convergence of fractal encoding algorithm
In this paper, we present a graph-theoretic interpretation of convergence of fractal encoding based on partial iterated function system (PIFS). First we have considered a special circumstance, where no spatial contraction has been allowed in the encoding process. The concept leads to the development of a linear time fast decoding algorithm from the compressed image. This concept is extended for the general scheme of fractal compression allowing spatial contraction (on averaging) from larger domains to smaller ranges. A linear time fast decoding algorithm is also proposed in this situation, which produces a decoded image very close to the result obtained by an ordinary iterative decompression algorithm.
29763170
Fractal image compression: a randomized approach
The fractal image compression is inherently time intensive and thus there is a growing need to increase its speedup. This paper presents a fast encoding scheme where the domain blocks are searched randomly for every range block. An analysis of the basic fractal image compression method has been carried out and on the basis of the analysis a method for image characterization has been proposed. Then a detailed theoretical analysis of this randomized fractal compression scheme has been presented and the results are validated with the experimental ones. The random approach has been found to be quite efficient giving considerable increase in speed-up compared to several other approaches.
5608135
Low-power weighted pseudo-random BIST using special scan cells
In this paper, a technique for weighted pseudo-random built-in self-test (BIST) of VLSI circuits is proposed, which uses special scan cells and a new weight selection algorithm to achieve low power dissipation. It is based on weighted pseudo-random scan testing in which only 3 weight values are used - 2 fixed values (0 or 1) and 1 random value (0.5). A new weight selection algorithm is used to select a set of weights that achieves high fault coverage while reducing power. The idea is to minimize power by careful selection of the set of scan cells having fixed values (0 or 1) in order to reduce switching activity. To implement this in hardware, a new scan cell design is proposed that can do scan and capture in the normal mode as well as fixed-bit mode. The new scan cell hardware increases the area of a typical circuit by less than 4%, but reduces power by as much as 96%, as indicated in experiments performed on benchmark circuits.