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.
18143775
An extended link reversal protocol in dynamic networks
We consider the problem of maintaining routing paths between nodes in a dynamic network. Gafni and Bertsekas proposed a link reversal approach called the BG method that maintains a directed acyclic graph (DAG) with a given destination as the sink node. By virtue of built-in redundancy, an updating algorithm to establish a new DAG is activated infrequently and it happens only when the last outgoing link of a host in the DAG is destroyed due to the movement of nodes. In this paper, we propose another updating approach that tries to minimize the total number of reversed links and to maintain routing information without using much extra overhead. The approach maintains a reversed breadth-first tree. Nodes in the network are either marked (inside the tree) or unmarked (outside the tree). When it is too costly to maintain a minimum path for a marked node, the branch rooted at the node is trimmed and the approach then gracefully switches to the BG method. Several extensions are also discussed. A simulation study is conducted to compare the performance of the proposed approach with the existing one.
38235672
Fault-Tolerant Resource Placement in Balanced Hypercubes
Balanced hypercube is a variant of the standard hypercube structure for multicomputers, with desirable properties of strong connectivity, regularity, and symmetry. This structure is a special type of load balancing graph designed to tolerate processor failure. In a balanced hypercube, each processor has a backup (matching) processor that shares the same set of neighboring nodes. Therefore, tasks that run on a faulty processor can be reactivated in the backup processor to provide efficient system reconfiguration. In this paper, we study the resource placement problem in balanced hypercubes. Resources can be hardware devices (such as I/O processors, disks, etc.) or software modules (such as data files, library routines, etc.). The proposed placement algorithm guarantees that each node without resource copy connects two (matching) nodes with resource copy. Therefore, one of the resource copies at each matching pair can be used as a backup to tolerate possible processor failures. Moreover, the proposed algorithm is perfect in that each node without resource copy connects two and only two nodes with resource copy and neighbors of each node with resource copy are nodes without resource copy.
5896177
Fault-tolerant routing in meshes/tori using planarly constructed fault blocks
A few faulty nodes can make an n-dimensional mesh or torus network unsafe for fault-tolerant routing methods based on the block fault model, where the whole system (n-dimensional space) forms a fault block. A new concept, called extended local safety information in meshes or tori, is proposed to guide fault-tolerant routing, and classifies fault-free nodes inside 2-dimensional planes. Many nodes globally marked as unsafe become locally enabled inside 2-dimensional planes. A fault-tolerant routing algorithm based on extended local safety information is proposed for k-ary n-dimensional meshes/tori. Our method does not need to disable any fault-free nodes, unlike many previous methods, and this enhances the computational power of the system and improves performance of the routing algorithm greatly. All fault blocks are constructed inside 2-dimensional planes rather than in the whole system. Extensive simulation results are presented and compared with the previous methods.
2242460
Deadlock-Free Multicasting in Irregular Networks Using Prefix Routing
A deadlock-free multicast scheme called prefix multicasting in irregular networks (i.e., networks with irregular topology) is studied. In prefix routing, a compact routing table is associated with each node (processor). Basically, each outgoing channel of a node is assigned a special label and an outgoing channel is selected if its label is a prefix of the label of the destination node. Node and channel labelling in an irregular network is based on a pre-defined spanning tree which may or may not be minimum. The routing process follows a two-phase process of going up and then down along the spanning tree, with a possible cross channel between two branches of the tree between two phases. It is shown that the proposed routing scheme is deadlock- and livelock-free. The approach is extended to multicasting in which the multicast packet is first forwarded up the tree to the longest common prefix (LCP) of destinations in the multicast. The packet is then treated as a multi-head worm that can split at branches of the spanning tree as the packet is sent down the tree.
11143071
Cluster-based intelligent searching in unstructured peer-to-peer networks
Existing cluster-based searching schemes in unstructured peer-to-peer (P2P) networks employ flooding/random forwarding on connected dominating sets (CDS) of networks. There exists no upper bound on the size of CDS of a network. Both flooding and CDS hinder query efficiency. Random forwarding worsens the recall ratio. In this paper, we propose a cluster-based searching scheme that intelligently forward queries on the maximum independent sets (MIS) of networks. Our approach partitions the entire network into disjoint clusters with one clusterhead (CH) per cluster. CHs form a MIS and are connected through gateway nodes. Each node fakes one role, a CH, a gateway, or an ordinary node. A CH looks up the data for the entire cluster using data summaries of cluster members, which are represented by bloom filters. Between clusters, CHs intelligently forward queries via gateways to the best neighbor CHs that are most likely to return query results. The experimental results demonstrate that our scheme greatly improves the query efficiency without degrading the quality of the query results, compared to existing approaches.
10373687
The effect of dissipation on solutions of the complex KdV equation
It is known that some periodic solutions of the complex KdV equation with smooth initial data blow up in finite time. In this paper, we investigate the effect of dissipation on the regularity of solutions of the complex KdV equation. It is shown here that if the initial datum is comparable to the dissipation coefficient in the L2-norm, then the corresponding solution does not develop any finite-time singularity. The solution actually decays exponentially in time and becomes real analytic as time elapses. Numerical simulations are also performed to provide detailed information on the behavior of solutions in different parameter ranges.
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.
59008197
A cycle-based rerouting scheme for wavelength-routed WDM networks
In this paper, we propose a cycle-based rerouting scheme (CRS) for link restoration in wavelength-routed WDM networks. The CRS is based on a scalable set of Pcycles covering a mesh network. Instead of using these Pcycles as enhanced self-healing rings as in traditional approaches, the CRS decomposes these P-cycles into rerouting paths for the links that the P-cycles cover. In this way, flexible and cost-effective link restoration is achieved. Simulation results show that the rerouting performance of the CRS can be very close to the theoretical upper bound.
32836256
Minimum average distance of strong orientations of graphs
The average distance of a graph (strong digraph) G, denoted by µ(G) is the average, among the distances between all pairs (ordered pairs) of vertices of G. If G is a 2-edge-connected graph, then µ→min(G) is the minimum average distance taken over all strong orientations of G. A lower bound for µ→min(G) in terms of the order, size, girth and average distance of G is established and shown to be sharp for several complete multipartite graphs. It is shown that there is no upper bound for µ→min(G) in terms of µ(G). However, if every edge of G lies on 3-cycle, then it is shown that µ→min(G) ≤ 7/4 µ(G). This bound is improved for maximal planar graphs to 5/3 µ(G) and even further to 3/2 µ(G) for eulerian maximal planar graphs and for outerplanar graphs with the property that every edge lies on 3-cycle. In the last case the bound is shown to be sharp.
7567561
Genetic Algorithm based Selective Neural Network Ensemble
Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual networks in some cases. Therefore an approach named GASEN is proposed, which trains several individual neural networks and then employs genetic algorithm to select an optimum subset of individual networks to constitute an ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e. averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost.