Architectural Considerations for a Self-Configuring Routing Scheme for Spontaneous Networks

Decoupling the permanent identifier of a node from the node's topology-dependent address is a promising approach toward completely scalable self-organizing networks. A group of proposals that have adopted such an approach use the same structure to: address nodes, perform routing, and implement location service. In this way, the consistency of the routing protocol relies on the coherent sharing of the addressing space among all nodes in the network. Such proposals use a logical tree-like structure where routes in this space correspond to routes in the physical level. The advantage of tree-like spaces is that it allows for simple address assignment and management. Nevertheless, it has low route selection flexibility, which results in low routing performance and poor resilience to failures. In this paper, we propose to increase the number of paths using incomplete hypercubes. The design of more complex structures, like multi-dimensional Cartesian spaces, improves the resilience and routing performance due to the flexibility in route selection. We present a framework for using hypercubes to implement indirect routing. This framework allows to give a solution adapted to the dynamics of the network, providing a proactive and reactive routing protocols, our major contributions. We show that, contrary to traditional approaches, our proposal supports more dynamic networks and is more robust to node failures

DHT-based functionalities using hypercubes

Decoupling the permanent identifi er of a node from the node's topology-dependent address is a promising approach toward completely scalable self-organizing networks. Existing solutions use a logical tree-like structure that, although allowing for simple address assignment and management, lead to low route selection flexibility. This clearly results in low routing performance and poor resilience to failures. In this paper, we propose to increase the number of candidate paths by using incomplete hypercubes. We will see that this solution can cover a wide range of applications by adapting to the dynamics of the network1st IFIP International Conference on Ad-Hoc NetWorkingRed de Universidades con Carreras en Informática (RedUNCI

Investigation of reduced hypercube (RH) networks : embedding and routing capabilities

The choice of a topology for the interconnection of resources in a distributed-memory parallel computing system is a major design decision. The direct binary hypercube has been widely used for this purpose due to its low diameter and its ability to efficiently emulate other important structures. The aforementioned strong properties of the hypercube come at the cost of high VLSI complexity due to the increase in the number of communication ports and channels per node with an increase in the total number of nodes. The reduced hypercube (RH) topology, which is obtained by a uniform reduction in the number of links for each hypercube node, yields lower complexity interconnection networks compared to hypercubes with the same number of nodes, thus permitting the construction of larger parallel systems. Furthermore, it has been shown that the RH at a lower cost achieves performance comparable to that of a regular hypercube with the same number of nodes. A very important issue for the viability of the RH is to investigate the efficiency of embedding frequently used topologies into it. This thesis proposes embedding algorithms for three very important topologies, namely the ring, the torus and the binary tree. The performance of the proposed algorithms is analyzed and compared to that of equivalent embedding algorithms for the regular hypercube. It is shown that these topologies are emulated efficiently on the RH. Additionally, two already proposed routing algorithms for the RH are evaluated through simulation results

A novel QoS multicast model in mobile ad hoc networks

2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Efficient embedding of virtual hypercubes in irregular WDM optical networks

This thesis addresses one of the important issues in designing future WDM optical networks. Such networks are expected to employ an all-optical control plane for dissemination of network state information. It has recently been suggested that an efficient control plane will require non-blocking communication infrastructure and routing techniques. However, the irregular nature of most WDM networks does not lend itself to efficient non-blocking communications. It has been recently shown that hypercubes offer some very efficient non-blocking solutions for, all-to-all broadcast operations, which would be very attractive for control plane implementation. Such results can be utilized by embedding virtual structures in the physical network and doing the routing using properties of a virtual architecture. We will emphasize the hypercube due to its proven usefulness. In this thesis we propose three efficient heuristic methods for embedding a virtual hypercube in an irregular host network such that each node in the host network is either a hypercube node or a neighbor of a hypercube node. The latter will be called a “satellite” or “secondary” node. These schemes follow a step-by-step procedure for the embedding and for finding the physical path implementation of the virtual links while attempting to optimize certain metrics such as the number of wavelengths on each link and the average length of virtual link mappings. We have designed software that takes the adjacency list of an irregular topology as input and provides the adjacency list of a hypercube embedded in the original network. We executed this software on a number of irregular networks with different connectivities and compared the behavior of each of the three algorithms. The algorithms are compared with respect to their performance in trying to optimize several metrics. We also compare our algorithms to an already existing algorithm in the literature

Processor allocation strategies for modified hypercubes

Parallel processing has been widely accepted to be the future in high speed computing. Among the various parallel architectures proposed/implemented, the hypercube has shown a lot of promise because of its poweful properties, like regular topology, fault tolerance, low diameter, simple routing, and ability to efficiently emulate other architectures. The major drawback of the hypercube network is that it can not be expanded in practice because the number of communication ports for each processor grows as the logarithm of the total number of processors in the system. Therefore, once a hypercube supercomputer of a certain dimensionality has been built, any future expansions can be accomplished only by replacing the VLSI chips. This is an undesirable feature and a lot of work has been under progress to eliminate this stymie, thus providing a platform for easier expansion. Modified hypercubes (MHs) have been proposed as the building blocks of hypercube-based systems supporting incremental growth techniques without introducing extra resources for individual hypercubes. However, processor allocation on MHs proves to be a challenge due to a slight deviation in their topology from that of the standard hypercube network. This thesis addresses the issue of processor allocation on MHs and proposes various strategies which are based, partially or entirely, on table look-up approaches. A study of the various task allocation strategies for standard hypercubes is conducted and their suitability for MHs is evaluated. It is shown that the proposed strategies have a perfect subcube recognition ability and a superior performance. Existing processor allocation strategies for pure hypercube networks are demonstrated to be ineffective for MHs, in the light of their inability to recognize all available subcubes. A comparative analysis that involves the buddy strategy and the new strategies is carried out using simulation results

Optimal cube-connected cube multiprocessors

Many CFD (computational fluid dynamics) and other scientific applications can be partitioned into subproblems. However, in general the partitioned subproblems are very large. They demand high performance computing power themselves, and the solutions of the subproblems have to be combined at each time step. The cube-connect cube (CCCube) architecture is studied. The CCCube architecture is an extended hypercube structure with each node represented as a cube. It requires fewer physical links between nodes than the hypercube, and provides the same communication support as the hypercube does on many applications. The reduced physical links can be used to enhance the bandwidth of the remaining links and, therefore, enhance the overall performance. The concept and the method to obtain optimal CCCubes, which are the CCCubes with a minimum number of links under a given total number of nodes, are proposed. The superiority of optimal CCCubes over standard hypercubes was also shown in terms of the link usage in the embedding of a binomial tree. A useful computation structure based on a semi-binomial tree for divide-and-conquer type of parallel algorithms was identified. It was shown that this structure can be implemented in optimal CCCubes without performance degradation compared with regular hypercubes. The result presented should provide a useful approach to design of scientific parallel computers

On Sensor Network Localization Using SDP Relaxation

A Semidefinite Programming (SDP) relaxation is an effective computational method to solve a Sensor Network Localization problem, which attempts to determine the locations of a group of sensors given the distances between some of them [11]. In this paper, we analyze and determine new sufficient conditions and formulations that guarantee that the SDP relaxation is exact, i.e., gives the correct solution. These conditions can be useful for designing sensor networks and managing connectivities in practice. Our main contribution is twofold: We present the first non-asymptotic bound on the connectivity or radio range requirement of the sensors in order to ensure the network is uniquely localizable. Determining this range is a key component in the design of sensor networks, and we provide a result that leads to a correct localization of each sensor, for any number of sensors. Second, we introduce a new class of graphs that can always be correctly localized by an SDP relaxation. Specifically, we show that adding a simple objective function to the SDP relaxation model will ensure that the solution is correct when applied to a triangulation graph. Since triangulation graphs are very sparse, this is informationally efficient, requiring an almost minimal amount of distance information. We also analyze a number objective functions for the SDP relaxation to solve the localization problem for a general graph.Comment: 20 pages, 4 figures, submitted to the Fields Institute Communications Series on Discrete Geometry and Optimizatio