Hypertree network

About: Hypertree network is a research topic. Over the lifetime, 76 publications have been published within this topic receiving 757 citations.

Hypertree: A Multiprocessor Interconnection Topology

Abstract: A new interconnection topology for incrementally expansible multicomputer systems is described, which combines the easy expansibility of tree structures with the compactness of the n-dimensional hypercube. The addition of n-cube links to the binary tree structure provides direct paths between nodes which have frequent data exchange in algorithms such as sorting and fast Fourier transforms (FFT's). The derivation of a family of such Hypertree structures is outlined, and the basic properties such as average path length, uniformity of the distribution of message traffic, and routing algorithms are analyzed.

A comparison of network coding and tree packing

Abstract: Network coding solutions and routing solutions, namely packing distribution trees, for the problem of information multicast is compared in this paper. To enable the comparison, we develop greedy tree packing algorithms that repeatedly pack the maximum-rate distribution tree and a greedy tree packing algorithm based on Lovasz' proof to Edmonds' theorem. We then investigate the potential advantages of network coding over routing. In terms of throughput, tree packing performs comparably to network coding on the network graphs of six Internet service providers. However, network coding offers additional benefits, including fewer network resources consumed, ease of management, and robustness.

System and method for automatic generation of a hierarchical tree network and the use of two complementary learning algorithms, optimized for each leaf of the hierarchical tree network

Abstract: A system and method that generates a hierarchical tree network and uses linear-plus-nonlinear learning algorithms to form a consensus view on a member’s future health status Each least in the hierarchical tree network is homogeneous in clinical characteristics, experience period, and available data assets Optimization is performed on each leaf so that features and learning algorithms can be tailored to local characteristics specific to each leaf

Hierarchical Inference of Unicast Network Topologies Based on End-to-End Measurements

Abstract: In this paper, we address the problem of topology discovery in unicast logical tree networks using end-to-end measurements. Without any cooperation from the internal routers, topology estimation can be formulated as hierarchical clustering of the leaf nodes based on pairwise correlations as similarity metrics. Unlike previous work that first assumes the network topology is a binary tree and then tries to generalize to a nonbinary tree, we provide a framework that directly deals with general logical tree topologies. A hierarchical algorithm to estimate the topology is developed in a recursive manner by finding the best partitions of the leaf nodes level by level. Our simulations show that the algorithm is more robust than binary-tree based methods

Tradeoffs for selection in distributed networks (Preliminary Version)

Abstract: Algorithms are presented for selecting an element of given rank from a set of elements distributed among the nodes of a network. Network topologies considered are a ring, a mesh, and a complete binary tree. For the ring and the mesh, upper bound tradeoffs are identified between the number of messages transmitted and the total delay due to message transmission. For the tree, an algorithm is presented that uses an asymptotically optimal number of messages.