2642909
An efficient distributed mutual exclusion algorithm based on relative consensus voting
Summary form only given. Many algorithms for achieving mutual exclusion in distributed computing systems have been proposed. The three most often used performance measures are the number of messages exchanged between the nodes per critical section (CS) execution, the response time, and the synchronization delay. We present a new fully distributed mutual exclusion algorithm. A node requesting the CS sends out the request message which will roam in the network. The message are forwarded among the nodes until the requesting node obtains enough permissions to decide its order to enter the CS. The decision is made by using relative consensus voting (RCV), which is a variation of the well-known majority consensus voting (MCV) scheme. Unlike existing algorithms which determine the node to enter the CS one by one, in our algorithm, several nodes can be decided and ordered for executing the CS. The synchronization delay is minimal. Although the message complexity can be up to O(N) in the worst case in a system with N nodes, our simulation results show that, on average, the algorithm needs less number of messages and has less response time than most of those existing algorithms which do not require a logical topology imposed on the nodes. This is especially true when the system is under heavy demand. Another feature of the proposed algorithm is that it does not require the FIFO property of the underlying message passing mechanism.
16257632
XYZ: a scalable, partially centralized lookup service for large-scale peer-to-peer systems
Peer-to-peer (P2P) systems are characterized by direct access between peer computers, rather than through a centralized server. File sharing is the dominant P2P application on the Internet, allowing users to easily contribute, search and obtain content. P2P systems can be categorized by the degrees of centralization. For fully centralized systems, the lookup service will not be available when the central directory server is down. For purely decentralized and unstructured systems, since there is no information about which nodes are likely to have the relevant files, searching essentially amounts to random search. This makes the lookup service unscalable and unpredictable. The objective of this paper is to design a partially centralized, scalable and self-organizing lookup service (XYZ) for wide area P2P systems. A clustering method is used to create the system backbone by connecting the cluster heads together and a color clustering method is adopted to create color overlays and minimize the searching space. Simulations and analysis are also provided. Extensions are proposed to achieve better performance
7582647
On self-similarity and hamiltonicity of dual-cubes
The dual-cube is a newly proposed topology for interconnection networks, which uses low dimensional hypercubes as building blocks. The primary advantages of the dual-cube over the hypercube are that, with the same node degree n, the dual-cube contains 2/sup n-1/ times more nodes than the hypercube and, with the same amount of nodes, the dual-cube has approximately 50% less links than the hypercube. This paper was focused on the investigations of the structural self-similarity and the Hamiltonian property of the dual-cube. It was shown that a dual-cube can be recursively constructed from lower dimensional dual-cubes and, conversely, a dual-cube can be recursively decomposed into lower dimensional dual-cubes. It was also proved that all dual-cubes are Hamiltonian. There exist multiple Hamiltonian cycles on a dual-cube, among which, [(n-1)/2] cycles are edge-disjoint. It was illustrated that rings and linear arrays can be effectively emulated on dual-cubes. Some strategies for track sharing in efficient VLSI layout design were also discussed.
2254959
Local-Safety-Information-Based Broadcasting in Hypercube Multicomputers with Node and Link Faults
This paper presents a method to cope with fault-tolerant broadcasting in hypercube multicomputers with both node and link faults. The local safety concept is extended to faulty hypercubes with both node and link faults. The local-safety-based algorithm is used in a fully unsafe hypercube, where there is no safe node. A fully unsafe hypercube can be split into a set of maximal safe subcubes. We show that if these maximal safe subcubes meet certain requirements given in the paper, broadcasting can still be carried out successfully and in some case optimal broadcast is still possible. The method is extended to fault-tolerant routing and multicasting when the system contains both node and link faults.
27746153
Broadcasting in Faulty Cube-Connected-Cycles with Minimum Recovery Time
We propose a broadcasting algorithm for a faulty Cube-Connected-Cycles multiprocessor. The broadcasting algorithm delivers a message to all the nodes non-redundantly using a binomial tree. The traffic generated because of the recovery is guaranteed to be minimum.
17371165
QoS Routing in Hypercube Multicomputers
In this paper, we present a coding method to capture QoS information in hypercube multicomputers. The coding method is based on a localized algorithm where each node interacts with its neighbors to gather QoS information. Specifically, each node maintains a QoS vector where the k-th element represents the guaranteed QoS performance to a destination that is k hops away. The localized algorithm exhibits desirable properties of self-stabilizing, self-optimizing, and self-healing. Simulation results show that this coding method provides a good approximation of the minimum QoS value to a k-hop destination and, at the same time, uses a relatively small number of packets to propagate a change in link state (QoS value) compared with the classical distributed Bellman-Ford method.
412675
Linear Asymmetric Classifier for cascade detectors
The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.
5787542
Relaxed game chromatic number of trees and outerplanar graphs
Abstract This paper studies the relaxed game chromatic number of trees and outerplanar graphs. It is proved that if G is a tree then χ g d ( G )⩽2 for d ⩾2. If G is an outerplanar graph, then χ g d ( G )⩽5 for d ⩾2, and χ g d ( G )⩾3 for d ⩽4.
2622485
Diagnostic test generation for sequential circuits
Efficient diagnosis of faults in VLSI circuits requires high quality diagnostic test sets. In this work novel techniques for diagnostic test generation are proposed that require significantly less time than previous methods. The set of fault pairs left undistinguished by a detection-oriented test set is first filtered to target only testable faults. Then diagnostic test generation is performed using a genetic algorithm (GA) combined with a diagnostic fault simulator. A new fitness metric is proposed for the GA that accurately measures the quality of candidate sequences while requiring a limited amount of CPU time. Experimental results illustrate the effectiveness of the approach for sequential circuits.
17480069
Similarity-based Web service matchmaking
With the increasing growth in popularity of Web services, matchmaking of relevant Web services becomes a significant challenge. Commonly, Web service is described by WSDL and published on UDDI registers. UDDI provides limited search facilities allowing only a keyword-based search of businesses, services, and the so called tModels based on names and identifiers. This category-based keyword-browsing method is clearly insufficient. Semantic Web service uses DAML-S instead of WSDL to represent capabilities of Web services. This improvement enables software agents or search engines to automatically find appropriate Web services via ontologies and reasoning algorithm enriched methods. However, the high cost of formally defining to the heavy and complicated services makes this improvement widespread adoption unlikely. To cope with these limitations, we have developed a suite of methods which assesses the similarity of Web services to achieve matchmaking. In particular, we present a conceptual model which classifies properties of Web services into four categories. For each category, a similarity assessment method has been given. In Web service matchmaking process, these similarity assessment methods can be used together or individually. Experiments highlight complementary contributions that our work makes to facilitate Web service matchmaking.
1591692
Classifier Combination for Improved Lexical Disambiguation
One of the most exciting recent directions in machine learning is the discovery that the combination of multiple classifiers often results in significantly better performance than what can be achieved with a single classifier. In this paper, we first show that the errors made from three different state of the art part of speech taggers are strongly complementary. Next, we show how this complementatry behavior can be used to our advantage. By using contextual cues to guide tagger combination, we are able to derive a new tagger that achieves performance significantly greater than any of the individual taggers.