A multipath analysis of biswapped networks.

Biswapped networks of the form $Bsw(G)$ have recently been proposed as interconnection networks to be implemented as optical transpose interconnection systems. We provide a systematic construction of $\kappa+1$ vertex-disjoint paths joining any two distinct vertices in $Bsw(G)$, where $\kappa\geq 1$ is the connectivity of $G$. In doing so, we obtain an upper bound of $\max\{2\Delta(G)+5,\Delta_\kappa(G)+\Delta(G)+2\}$ on the $(\kappa+1)$-diameter of $Bsw(G)$, where $\Delta(G)$ is the diameter of $G$ and $\Delta_\kappa(G)$ the $\kappa$-diameter. Suppose that we have a deterministic multipath source routing algorithm in an interconnection network $G$ that finds $\kappa$ mutually vertex-disjoint paths in $G$ joining any $2$ distinct vertices and does this in time polynomial in $\Delta_\kappa(G)$, $\Delta(G)$ and $\kappa$ (and independently of the number of vertices of $G$). Our constructions yield an analogous deterministic multipath source routing algorithm in the interconnection network $Bsw(G)$ that finds $\kappa+1$ mutually vertex-disjoint paths joining any $2$ distinct vertices in $Bsw(G)$ so that these paths all have length bounded as above. Moreover, our algorithm has time complexity polynomial in $\Delta_\kappa(G)$, $\Delta(G)$ and $\kappa$. We also show that if $G$ is Hamiltonian then $Bsw(G)$ is Hamiltonian, and that if $G$ is a Cayley graph then $Bsw(G)$ is a Cayley graph

A systematic approach to reliable multistage interconnection network design

Bibliography: p. 34-35.Army Research Office grant no. DAAG29-84-K-0005 Advanced Research Projects Agency monitored by ONR, contract N00014-81-K-0742C.-C. Jay Kuo

Adaptive remote visualization system with optimized network performance for large scale scientific data

This dissertation discusses algorithmic and implementation aspects of an automatically configurable remote visualization system, which optimally decomposes and adaptively maps the visualization pipeline to a wide-area network. The first node typically serves as a data server that generates or stores raw data sets and a remote client resides on the last node equipped with a display device ranging from a personal desktop to a powerwall. Intermediate nodes can be located anywhere on the network and often include workstations, clusters, or custom rendering engines. We employ a regression model-based network daemon to estimate the effective bandwidth and minimal delay of a transport path using active traffic measurement. Data processing time is predicted for various visualization algorithms using block partition and statistical technique. Based on the link measurements, node characteristics, and module properties, we strategically organize visualization pipeline modules such as filtering, geometry generation, rendering, and display into groups, and dynamically assign them to appropriate network nodes to achieve minimal total delay for post-processing or maximal frame rate for streaming applications. We propose polynomial-time algorithms using the dynamic programming method to compute the optimal solutions for the problems of pipeline decomposition and network mapping under different constraints. A parallel based remote visualization system, which comprises a logical group of autonomous nodes that cooperate to enable sharing, selection, and aggregation of various types of resources distributed over a network, is implemented and deployed at geographically distributed nodes for experimental testing. Our system is capable of handling a complete spectrum of remote visualization tasks expertly including post processing, computational steering and wireless sensor network monitoring. Visualization functionalities such as isosurface, ray casting, streamline, linear integral convolution (LIC) are supported in our system. The proposed decomposition and mapping scheme is generic and can be applied to other network-oriented computation applications whose computing components form a linear arrangement

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor