Architecture-dependent tuning of the parameterized communication model for optimal multicasting

In this paper, we consider scheduling in multicast switching networks, which aims to minimize the multicast latency for a set of multicast requests. Such a problem has been proved to be NP-complete. We propose a simple, fast greedy multicast scheduling algorithm and derive a lower bound and an upper bound on the performance of the algorithm. As can be seen, while a lower bound is fairly straightforward, the upper bound is much more difficult to obtain. By translating the multicast scheduling problem into a graph theory problem and employing a random graph approach, we are able to obtain a probabilistic upper bound on the performance of the multicast scheduling algorithm. Our analytical and simulation results show that the performance of the proposed multicast scheduling algorithm is quite close to the lower bound and is statistically guaranteed by the probabilistic upper bound.

Because low Earth orbit (LEO) satellites provide short round-trip delay, they are becoming increasingly important for providing real-time applications such as voice and video traffic to global coverage. Multicast communication that simultaneously transmits messages from a source to a group of destination is essential for future LEO satellite networks. The multicast routing problem in terrestrial networks has already been studied extensively in the past. In LEO networks, previous research on the multicast routing only considers the network cost optimization without satisfying the delay requirement. This paper proposes a distributed multicast tree algorithm with sub-optimal network cost under the delay bound in LEO satellite networks. We compare the performance of our MC algorithm when applied to the LEO satellite networks with the periodically time-variant ISL sub-network. The algorithms are judged based on the quality of MC trees it generates and its efficiency in managing the network resources. Simulation results show that our delay-constrained multicast algorithms are capable of fulfilling the delay requirements in LEO satellite networks.

This paper proposes a tabu search (TS) based optimization approach to search a minimum-interference multicast tree which satisfies the end-to-end delay constraint and optimizes the usage of the scarce radio network resource in wireless mesh networks. The path-oriented encoding method is adopted and each candidate solution is represented by a tree data structure (i.e., a set of paths). Since we expect the multicast trees on which the minimum-interference channel assignment can be produced, a fitness function that returns the total channel conflict is devised. The techniques for controlling the tabu search procedure are well developed. A simple yet effective channel assignment algorithm is proposed to reduce the channel conflict. Simulation results show that the proposed TS multicast algorithm can produce the multicast trees which have better performance in terms of both the total channel conflict and the tree cost than that of a well known multicast algorithm in wireless mesh networks.

In this paper, a multi-group multi-way relaying network is considered. Multiple half-duplex nodes form a group and each node wants to share its message with all other nodes in its group via an intermediate half-duplex relay. The nodes of the whole network are equally distributed over the groups. In this paper, we propose a multicast interference alignment algorithm for a multi-group multi-way relay network, in which the minimum required number of antennas at the relay is independent of the number of nodes per group. This is an important property, because physical antenna resources are limited in general. In order to achieve this, we consider a transmission scheme with several multiple access phases and several multicast phases. In each of the multicast phases we create a MIMO interference multicast channel, by separating the antennas of the relay into clusters. Each of these clusters serves a specific group of nodes and transmits in such a way that the signals transmitted from different clusters are aligned at the non-intended multicast groups. It is shown that the proposed multicast algorithm outperforms a reference algorithm from the literature for a broad range of Signal-to-Noise Ratio (SNR) values, while still requiring less antennas at the relay.

This paper presents a new heuristic algorithm called DDBMB (dynamic delay bounded multicast algorithm) to construct a minimum cost multicast tree. The heuristic depends on: (A) bounded delay along paths from source node to each destination node; (B) minimum cost of the multicast tree where the algorithm starts with the minimum delay tree and monotonically decreases the cost iteratively to construct a delay bounded minimum cost tree; and (C) dynamic multicast tree status which is maintained by updating the existing multicast tree when nodes in the network request join or leave. The construction of such constrained multicast tree is known as an NP-complete problem. Compared with other existing relative algorithms, the heuristic demonstrates a better average case behavior and can be very close to having a minimum cost depending on the delay bounds imposed.

Distributed applications using file sharing and file replication, conferencing, and delivery in multimedia systems often need to use the semantics of causal multicast. Causal multicast in a mobile system is complicated by the various limitations imposed by the mobile system. Though there are different directions to improve the performance of causal multicast algorithms in mobile systems, at the core of such algorithms is the module to impose causal delivery among a set of nodes, which may be either mobile hosts, or mobile service stations, or peers at a higher level in the interconnection hierarchy. We show how to adapt the optimal causal multicast algorithm of Kshemkalyani-Singhal to mobile networks, and then show simulation results comparing its performance with that of other algorithms for causal multicast in mobile networks.

Efficient routing of messages is a key to the performance of multicomputers. Multicast communication refers to the delivery of the same message from a source node to an arbitrary number of destination nodes. While multicast communication is highly demanded in many applications, most of the existing multicomputers do not directly support this service; rather it is indirectly supported by multiple one-to-one or broadcast communications, which result in more network traffic and a waste of system resources. In this paper, we study routing evaluation criteria for multicast communication under different switching technologies. Multicast communication in multicomputers is formulated as a graph theoretical problem. Depending on the evaluation criteria and switching technologies, we study three optimal multicast communication problems, which are equivalent to the finding of the following three subgraphs: optimal multicast path, optimal multicast cycle, and minimal Steiner tree, where the interconnection of a multicomputer defines a host graph. We will show that all these optimization problems are NP-complete for the popular 2D-mesh and hypercube host graphs. Heuristic multicast algorithms for these routing problems are proposed. © 1993 IEEE

Simplified models are widely applied in finite element computations regarding mechanical and structural problems. However, the simplified model sometimes causes many deviations in the finite element analysis (FEA) of structures, especially in the non-designed structures which have undergone unknowable deformation features. Hence, a novel FEA methodology based on the parametric model by approximating three-dimensional (3D) feature data is proposed to solve this problem in the present manuscript. Many significant and effective technologies have been developed to detect 3D feature information accurately, e.g., terrestrial laser scanning (TLS), digital photogrammetry, and radar technology. In this manuscript, the parametric FEA model combines 3D point clouds from TLS and the parametric surface approximation method to generate 3D surfaces and models accurately. TLS is a popular measurement method for reliable 3D point clouds acquisition and monitoring deformations of structures with high accuracy and precision. The B-spline method is applied to approximate the measured point clouds data automatically and generate a parametric description of the structure accurately. The final target is to reduce the effects of the model description and deviations of the FEA. Both static and dynamic computations regarding a composite structure are carried out by comparing the parametric and general simplified models. The comparison of the deformation and equivalent stress of future behaviors are reflected by different models. Results indicate that the parametric model based on the TLS data is superior in the finite element computation. Therefore, it is of great significance to apply the parametric model in the FEA to compute and predict the future behavior of the structures with unknowable deformations in engineering accurately.

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

We introduce a discrete-time model of a risky security’s future price using a binomial tree. By increasing the number of time-steps in the tree, the assumption is that one obtains a more and more accurate model of the random future price of a security. This chapter covers: the general binomial tree model of future security prices, the Cox-Ross-Rubinstein (CRR) tree in the real world and risk-neutral world, the Lindeberg Central Limit Theorem with applications to the continuous-time limit of the CRR tree, and statistical and probability formulas for continuous-time security prices.

Different communication models have been historically adopted in the automotive domain for allowing concurrent tasks to coordinate, interact and synchronize on a shared memory system for implementing complex functions, while ensuring data consistency and/or time determinism. To this extent, most automotive OSs provide inter-task communication and synchronization mechanisms based upon memory-sharing paradigms, where variables modified by one task may be concurrently accessed also by other tasks. A so-called “effect chain” is created when the effect of an initial event is propagated to an actuation signal through sequences of tasks writing/reading shared variables. The responsiveness, performance and stability of the control algorithms of an automotive application typically depend on the propagation delays of selected effect chains. Depending on the communication model adopted, the propagation delay of an effect chain may significantly vary, as may be the resulting overhead and memory footprint. In this paper, we explore the trade-offs between three communication models that are widely adopted for industrial automotive systems, namely, Explicit, Implicit, and Logical Execution Time (LET). A timing and schedulability analysis is provided for tasks scheduled following a mixed preemptive configuration, as specified in the AUTOSAR model. An end-to-end latency characterization is then proposed, deriving different latency metrics for effect chains under each one of the considered models. The results are compared against an industrial case study consisting of an automotive engine control system.

Accurate modeling of arterial response to physiological or pathological loads may shed light on the processes leading to initiation and progression of a number of vascular diseases and may serve as a tool for prediction and diagnosis. In this study, a microstructure based hyperelastic constitutive model is developed for passive media of porcine coronary arteries. The most general model contains 12 independent parameters representing the three-dimensional inner fibrous structure of the media and includes the effects of residual stresses and osmotic swelling. Parameter estimation and model validation were based on mechanical data of porcine left anterior descending (LAD) media under radial inflation, axial extension, and twist tests. The results show that a reduced four parameter model is sufficient to reliably predict the passive mechanical properties. These parameters represent the stiffness and the helical orientation of each lamellae fiber and the stiffness of the interlamellar struts interconnecting these lamellae. Other structural features, such as orientational distribution of helical fibers and anisotropy of the interlamellar network, as well as possible transmural distribution of structural features, were found to have little effect on the global media mechanical response. It is shown that the model provides good predictions of the LAD media twist response based on parameters estimated from only biaxial tests of inflation and extension. In addition, good predictive capabilities are demonstrated for the model behavior at high axial stretch ratio based on data of law stretches.

Effect of Heterogeneous Nodes Location on the Performance of Clustering Algorithms for Wireless Sensor Networks

The asymptotic thermodynamic behavior of fluids near the critical point is described by scaling laws with universal scaling functions that can be represented by parametric equations. In this paper, we derive a more general parametric model that incorporates the crossover from singular thermodynamic behavior near the critical point to regular classical thermodynamic behavior far away from the critical point. Using ethane as an example, we show that such a parametric crossover model yields an accurate representation of the thermodynamic properties of fluids in a large region around the critical point.

We study oblivious routing in fat-tree-based system area networks with deterministic routing under the assumption that the traffic demand is uncertain. The performance of a routing algorithm under uncertain traffic demands is characterized by the oblivious performance ratio that bounds the relative performance of the routing algorithm with respect to the optimal algorithm for any given traffic demand. We consider both single-path routing, where only one path is used to carry the traffic between each source-destination pair, and multipath routing, where multiple paths are allowed. For single-path routing, we derive lower bounds of the oblivious performance ratio for different fat-trees and develop routing schemes that achieve the optimal oblivious performance ratios for commonly used topologies. Our evaluation results indicate that the proposed oblivious routing schemes not only provide the optimal worst-case performance guarantees but also outperform existing schemes in average cases. For multipath routing, we show that it is possible to obtain an optimal scheme for all traffic demands (an oblivious performance ratio of 1). These results quantitatively demonstrate the performance difference between single-path routing and multipath routing in fat-trees.