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We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.
In this paper, we describe a top-down analysis and simulation approach to size the bandwidths of a store-andforward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. We use these techniques to estimate the wide area network (WAN) bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network.
Routing in Delay-/Disruption-Tolerant Networking (DTN) has long been recognized as a challenging research topic. The difficulty lies in the fact that link intermittency and network partitioning, possibly coupled with long delays, prevent the use of Internet solutions based on an up-to-date comprehensive knowledge of network topology, as communicated by routing protocols. In the literature on DTN routing, there is a dichotomy between solutions designed for deterministic (e.g., space flight) networks, such as Contact Graph Routing (CGR), and the wide variety of protocols designed for opportunistic terrestrial networks. After a discussion of the origin and motivations of this duality, the paper presents an opportunistic extension of CGR (OCGR). The aim is to try to resolve the DTN routing dichotomy by providing a unified approach suitable for all DTN environments.
Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.
Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, GEO, MEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes an architecture enabling the development and deployment of cognitive networking capabilities into the envisioned future NASA space communications infrastructure. We begin by discussing the need for increased automation, including inter-system discovery and collaboration. This discussion frames the requirements for an architecture supporting cognitive networking for future missions and relays, including both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture, and results of implementation and initial testing of a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.
Single tree-finding program for digital computer evaluation of topological formulas for network analysis
A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.
High-rate Delay Tolerant Networking (HDTN) is a performance-optimized Delay Tolerant Networking (DTN) implementation that can provide multigigabit per second data rates in disruptive and high-latency space networks. Routing, especially in large-scale space networks, remains challenging due to network topologies that evolve over time. This paper presents a simulation tool that enables HDTN implementation testing at accelerated speeds, which is key for routing in large-scale space networks.
This report describes the simulation of the overall communication network structure for the Advanced Solid Rocket Motor (ASRM) facility being built at Yellow Creek near Iuka, Mississippi as of today. The report is compiled using information received from NASA/MSFC, LMSC, AAD, and RUST Inc. As per the information gathered, the overall network structure will have one logical FDDI ring acting as a backbone for the whole complex. The buildings will be grouped into two categories viz. manufacturing intensive and manufacturing non-intensive. The manufacturing intensive buildings will be connected via FDDI to the Operational Information System (OIS) in the main computing center in B_1000. The manufacturing non-intensive buildings will be connected by 10BASE-FL to the OIS through the Business Information System (BIS) hub in the main computing center. All the devices inside B_1000 will communicate with the BIS. The workcells will be connected to the Area Supervisory Computers (ASCs) through the nearest manufacturing intensive hub and one of the OIS hubs. Comdisco's Block Oriented Network Simulator (BONeS) has been used to simulate the performance of the network. BONeS models a network topology, traffic, data structures, and protocol functions using a graphical interface. The main aim of the simulations was to evaluate the loading of the OIS, the BIS, and the ASCs, and the network links by the traffic generated by the workstations and workcells throughout the site.
The initial design of a supercomputer network topology including the design of the communications nodes along with the communications interface hardware and software is covered. Several space science applications that are proposed experiments by GSFC and JPL for a supercomputer network using the NASA ACTS satellite are also reported.
The North Alabama Lightning Mapping Array (LMA) is s a 3-D VHF regional lightning detection system that provides on-orbit algorithm validation and instrument performance assessments for the NASA Lightning Imaging Sensor, as well as information on storm kinematics and updraft evolution that offers the potential to improve severe storm warning lead time by up t o 50% and decrease te false alarm r a t e ( for non-tornado producing storms). In support of this latter function, the LMA serves as a principal component of a severe weather test bed to infuse new science and technology into the short-term forecasting of severe and hazardous weather, principally within nearby National Weather Service forecast offices. The LMA, which became operational i n November 2001, consists of VHF receivers deployed across northern Alabama and a base station located at the National Space Science and Technology Center (NSSTC), which is on t h e campus of the University of Alabama in Huntsville. The LMA system locates the sources of impulsive VHF radio signals s from lightning by accurately measuring the time that the signals aririve at the different receiving stations. Each station's records the magnitude and time of the peak lightning radiation signal in successive 80 ms intervals within a local unused television channel (channel 5, 76-82 MHz in our case ) . Typically hundreds of sources per flash can be reconstructed, which i n t u r n produces accurate 3-dimensional lightning image maps (nominally <50 m error within 150 la. range). The data are transmitted back t o a base station using 2.4 GHz wireless Ethernet data links and directional parabolic grid antennas. There are four repeaters in the network topology and the links have an effective data throughput rate ranging from 600 kbits s -1 t o 1.5 %its s -1. This presentation provides an overview of t h e North Alabama network, the data processing (both real-time and post processing) and network statistics.
Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.
The Mobile Data System, which is intended to provide for packet switched data services is currently under development. The system is based on a star network topology consisting of a centralized Data Hub (DH) serving a large number of mobile terminals. Through the Data Hub, end-to-end connections can be established between terrestrial users on public or private data networks and mobile users. The MDS network will be capable of offering a variety of services some of which are based on the standard X.25 network interface protocol, and others optimized for short messages and broadcast messages. A description of these services and the trade-offs in the DH design are presented.
Zirconium-based metal–organic frameworks (Zr-MOFs) exhibit remarkable structural diversity and functionality. However, uncovering new topological types within this family remains a considerable challenge today. Herein, we report two new hexa-topic ligands designed through the introduction of torsional flexibility, which enable the construction of two Zr-MOFs featuring rare network topologies. The (4,4′,4″,4‴,4‴′,4‴′′-((2-carboxybenzene-1,3,5-triyl)tris(9H-carbazole-9,3,6-triyl))hexabenzoic acid ligand (BTCH)) was obtained by replacing the rigid triptycene core in the H6PET-1 ligand (4,4′,4″,4‴,4‴′,4‴′′-(9,10-dihydro-9,10-[1,2]benzenoanthracene-2,3,6,7,14,15-hexayl)hexabenzoic acid) with a benzene-tricarbazole unit. Owing to its torsionally flexible core that allows rotational freedom to the arms, this ligand directs the construction of NU-2620 (NU represents Northwestern University), a Zr-MOF with 8-connected Zr6 clusters and the rare nuh topology. Further flexibilization of the carbazole units to benzene rings yielded the even more torsionally flexible 5′,5‴-bis(4-carboxyphenyl)-5″-(4,4″-dicarboxy-[1,1′:3′,1″-terphenyl]-5′-yl)-[1,1′:3′,1″:3″,1‴:3‴,1‴′-quinquephenyl]-4,4‴′-dicarboxylic acid ligand (CCTT), which forms NU-2630 featuring 6-connected clusters and the pcu topology. Both frameworks exhibit good chemical stability, prompting evaluation of their performance in CO2 photoreduction catalysis. Under low-concentration CO2 conditions, NU-2620 displays markedly higher catalytic activity than its benzene-based analogue, NU-2630, thanks to the abundance of photoactive carbazole units within its structure. These results demonstrate that introducing torsional flexibility in high-connected linkers can unlock access to new topologies and accelerates the reticular expansion of Zr-MOFs.
The ability to rapidly deploy inexpensive satellites to meet tactical goals has become an important goal for military space systems. In fact, Operationally Responsive Space (ORS) has been in the spotlight at the highest levels. The Office of the Secretary of Defense (OSD) has identified that the critical next step is developing the bus standards and modular interfaces. Historically, satellite components have been constructed based on bus standards and standardized interfaces. However, this has not been done to a degree, which would allow the rapid deployment of a satellite. Advancements in plug-and-play (PnP) technologies for terrestrial applications can serve as a baseline model for a PnP approach for satellite applications. Since SpaceWire (SpW) has become a de facto standard for satellite high-speed (greater than 200Mbp) on-board communications, it has become important for SpW to adapt to this Plug and Play (PnP) environment. Because SpW is simply a bulk transport protocol and lacks built-in PnP features, several changes are required to facilitate PnP with SpW. The first is for Host(s) to figure out what the network looks like, i.e., how pieces of the network, routers and nodes, are connected together; network mapping, and to receive notice of changes to the network. The second is for the components connected to the network to be understood so that they can communicate. The first element, network topology mapping & change of status indication, is being defined (topic of this paper). The second element describing how components are to communicate has been defined by ARFL with the electronic data sheets known as XTEDS. The first element, network mapping, is recent activities performed by Air Force Research Lab (ARFL), Naval Research Lab (NRL), NASA and US industry (Honeywell, Clearwater, FL, and others). This work has resulted in the development of a protocol that will perform the lower level functions of network mapping and Change Of Status (COS) indication required by Plug 'n' Play over SpaceWire. This work will be presented to the SpaceWire working group for standardization under European Cooperation for Space Standardization (ECSS) and to obtain a permanent Protocol ID (see SpaceWire Protocol ID: What Does it Mean to You; IEEE Aerospace Conference 2006). The portion of the Plug 'n' Play protocol that will be described in this paper is how the Host(s) of a SpaceWire network map the network and detect additions and deletions of devices on a SpaceWire network.
Microorganisms are shown to actively partition their intracellular resources, such as pro- teins, for growth optimization. Recent experiments have begun to reveal molecular com- ponents unpinning the partition; however, it remains unclear quantitatively how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation, and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that cellular ability of resource partition lies in an ultrasensitive, negative feedback control- ling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism which mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations, and is also efficient for biomass production over time. Furthermore, this work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.
Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized communication. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.
Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized elements. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.