Results for “Networking” — Page 29

The transition to new technology, innovative ideas, and resistance to change is something that every industry experiences. Recent examples of this shift are changing to using robots in the assembly line construction of automobiles or the increasing use of robotics for medical procedures. Most often this is done with cost-reduction in mind, though ease of use for the customer is also a driver. All industries experience the push to increase efficiency of their systems; National Aeronautics and Space Administration (NASA) and the commercial space industry are no different. NASA space communication services are provided by three separately designed, developed, maintained, and operated communications networks known as the Deep Space Network (DSN), Near Earth Network (NEN) and Space Network (SN). The Space Communications and Navigation (SCaN) Program is pursuing integration of these networks and has performed a variety of architecture trade studies to determine what integration options would be the most effective in achieving a unified user mission support organization, and increase the use of common operational equipment and processes. The integration of multiple, legacy organizations and existing systems has challenges ranging from technical to cultural. The existing networks are the progeny of the very first communication and tracking capabilities implemented by NASA and the Jet Propulsion Laboratory (JPL) more than 50 years ago and have been customized to the needs of their respective user mission base. The technical challenges to integrating the networks are many, though not impossible to overcome. The three distinct networks provide the same types of services, with customizable data rates, bandwidth, frequencies, and so forth. The differences across the networks have occurred in effort to satisfy their user missions' needs. Each new requirement has made the networks more unique and harder to integrate. The cultural challenges, however, have proven to be a significant obstacle for integration. Over the past few decades of use, user missions and network personnel alike have grown accustomed to the processes by which services are provided by the NASA communications and navigation networks. The culture established by each network has created several challenges that need to be overcome in order to effectively integrate the networks. As with any change, there has been resistance, an apprehension to explore automation of existing processes, and a working environment that attempts to indirectly influence change without mandating compliance. Overcoming technical and cultural challenges is essential to successfully integrating the networks and although the challenges are numerous, the integration of the networks promises a more efficient space communications network for NASA and its customers, as well as potential long-term cost savings to the agency. This paper, Challenges of Integrating NASA Legacy Communications Networks, will provide a brief overview of the current NASA space communications networks as well as the an overview of the process implemented while performing the SCaN Trade Studies and an introduction to the requirements driving integration of the SCaN Networks. This paper will describe in detail the challenges experienced, both technical and cultural, while working with NASA space communications network-specific personnel. The paper will also cover lessons learned during the performance of architecture trade studies and provide recommendations for ways to improve the process.

Reinert, Jessica M.↗

NASA NTRS2013

Challenges of Integrating NASA's Space Communications Networks

The transition to new technology, innovative ideas, and resistance to change is something that every industry experiences. Recent examples of this shift are changing to using robots in the assembly line construction of automobiles or the increasing use of robotics for medical procedures. Most often this is done with cost-reduction in mind, though ease of use for the customer is also a driver. All industries experience the push to increase efficiency of their systems; National Aeronautics and Space Administration (NASA) and the commercial space industry are no different. NASA space communication services are provided by three separately designed, developed, maintained, and operated communications networks known as the Deep Space Network (DSN), Near Earth Network (NEN) and Space Network (SN). The Space Communications and Navigation (SCaN) Program is pursuing integration of these networks and has performed a variety of architecture trade studies to determine what integration options would be the most effective in achieving a unified user mission support organization, and increase the use of common operational equipment and processes. The integration of multiple, legacy organizations and existing systems has challenges ranging from technical to cultural. The existing networks are the progeny of the very first communication and tracking capabilities implemented by NASA and the Jet Propulsion Laboratory (JPL) more than 50 years ago and have been customized to the needs of their respective user mission base. The technical challenges to integrating the networks are many, though not impossible to overcome. The three distinct networks provide the same types of services, with customizable data rates, bandwidth, frequencies, and so forth. The differences across the networks have occurred in effort to satisfy their user missions' needs. Each new requirement has made the networks more unique and harder to integrate. The cultural challenges, however, have proven to be a significant obstacle for integration. Over the past few decades of use, user missions and network personnel alike have grown accustomed to the processes by which services are provided by the NASA communications and navigation networks. The culture established by each network has created several challenges that need to be overcome in order to effectively integrate the networks. As with any change, there has been resistance, an apprehension to explore automation of existing processes, and a working environment that attempts to indirectly influence change without mandating compliance. Overcoming technical and cultural challenges is essential to successfully integrating the networks and although the challenges are numerous, the integration of the networks promises a more efficient space communications network for NASA and its customers, as well as potential long-term cost savings to the agency. This paper, Challenges of Integrating NASA Legacy Communications Networks, will provide a brief overview of the current NASA space communications networks as well as the an overview of the process implemented while performing the SCaN Trade Studies and an introduction to the requirements driving integration of the SCaN Networks. This paper will describe in detail the challenges experienced, both technical and cultural, while working with NASA space communications network-specific personnel. The paper will also cover lessons learned during the performance of architecture trade studies and provide recommendations for ways to improve the process.

Systems Integration↗

DOE OSTI2017

Using the D-Wave 2X Quantum Computer to Explore the Formation of Global Terrorist Networks

Social networks with signed edges (+/-) play an important role in an area of social network theory called structural balance. In these networks, edges represent relationships that are labeled as either friendly (+) or hostile (-). A signed social network is balanced only if all cycles of three or more nodes in the graph have an odd number of hostile edges. A fundamental property of a balanced network is that it can be cleanly divided into 2 factions, where all relationships within each faction are friendly, and all relationships between members of different factions are hostile. The more unbalanced a network is, the more edges will fail to adhere to this rule, making factions more ambiguous. Social theory suggests unbalanced networks should be unstable, a finding that has been supported by research on gangs, which shows that unbalanced relationships are associated with greater violence, possibly due to this increased ambiguity about factional allegiances (Nakamura et al). One way to estimate the imbalance in a network, if only edge relationships are known, is to assign nodes to factions that minimize the number of violations of the edge rule described above. This problem is known to be computationally NP-hard. However, Facchetti et al. have pointed out that it is equivalent to an Ising model with a Hamiltonian that effectively counts the number of edge rule violations. Therefore, finding the assignment of factions that minimizes energy of the equivalent Ising system yields an estimate of the imbalance in the network. Based on the Ising model equivalence of the signed-social network balance problem, we have used the D-Wave 2X quantum annealing computer to explore some aspects of signed social networks. Because connectivity in the D-Wave computer is limited to its particular native topology, arbitrary networks cannot be represented directly. Rather, they must be “embedded” using a technique in which multiple qubits are chained together with special weights to simulate a collection of nodes with the required connectivity. This limits the size of a fully connected network in the D-Wave to about 50 simulated nodes, using all of the approximately 1150 qubits in the machine. In order to keep within this limitation, while exploring a problem of potential social relevance, we constructed time series of historical network snapshots from Stanford’s Mapping Militants Project, where nodes represent militant organizations, and edges represent either alliances or rivalries between organizations. We constructed two series from different theaters – Iraq and Syria – spanning timelines from about 2000 to 2016, each with networks whose maximum size was in the 20-30 node range. Computationally, our experience suggests D-Wave technology is promising, providing fast, nearly constant scaling of computational effort in the main part of the calculation that relies on the quantum annealing cycle. However, the cost of embedding an arbitrary network of interest in the D-Wave native topology scales poorly. If the embedding cost can be amortized relative to the annealing cycle, it may be possible to gain a substantial advantage over classical computing methods, provided a large enough network can be accommodated by partitioning into subnetworks or some similar strategy. In terms of our application to networks of militant organizations, we found a rise in network imbalance in the Syrian theater that appears to correspond roughly with the entrance of the Islamic State into a milieu already populated with other groups, a phenomenon we plan to explore in more detail. In these very preliminary results, we also noticed that during at least one period where both the size and imbalance of the network increased substantially, the imbalance per edge seemed to remain fairly steady. This may suggest some adaptive behavior among the participating factions, which may also warrant further exploration.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

The Transactive Energy Network Template Metamodel

While transactive energy, which is defined as an allocation of electricity based on dynamically discovered values or prices, has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. Some basic principles of transactive energy are familiar from existing wholesale electricity markets. Locational prices are calculated today for zones within bulk electric transmission systems. Locational prices differ while accounting for the locational costs of electricity generation and the losses and constraints incurred when electricity is transmitted from generators and distributed to consumers. A transactive energy network might include these transmission zones. However, current research strives to apply transactive energy also in electricity distribution circuits, buildings, and even for individual generating and consuming devices. At the same time, researchers explore how to apply transactive energy in real time during increasingly shorter time intervals. Automated computational agents become necessary as transactive energy becomes applied to smaller circuit zones and at faster dynamic timescales. A transactive energy network is an example of a multi-agent system. Each zone in the network is represented by its transactive agent, which makes decisions for and acts on behalf of a business entity that is responsible for and manages one of the circuit regions. A transactive energy network is also an example of a decentralized, distributed control system. Control decisions and responsibilities are distributed among the network’s transactive agents. The transactive agents are independent; that is, there typically is no centralized authority or oversight function. Instead, transactive agents exchange transactive signals and thereby negotiate the prices and quantities of electricity that they will exchange. Initially, the circuit regions and responsibilities of transactive agents appear to be very dissimilar. Each circuit region may comprise transmission, distribution, or building-level circuits. Each has a unique position and electrical connectivity within the transactive energy network. Each possesses unique assets that either generate or consume electricity, and these (e.g., renewable energy generator, diesel generator, aggregate utility load, building load, space conditioning, refrigerator, etc.) may further differ in their price flexibility and in their strategies for responding to dynamic electricity prices. Given such diversity, an implementer’s first inclination might be to start from scratch to define all these devices and to engineer their seemingly unique interactions. Given that each implementer’s perspective may be narrow within a transactive energy network, it is unlikely that uniquely engineered systems would interact well. This is where the transactive network template is applicable. The transactive network template is a metamodel that has been developed to guide implementers as they configure their own transactive agent within a network of such agents. The object-oriented design of the transactive network template provides basic code object types that may be used and extended by implementers to represent each of the assets in their circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule power usage, balance electric supply and demand, and coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based1 reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. Version 2 of the transactive node template generalizes the market class and its methods to facilitate multiple, and more diverse market coordination mechanisms than were facilitated by and demonstrated using Version 1. Version 3 includes new Appendix B, which addresses the designs of methods that would make dynamic prices track approved electricity rates. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow. Development of the transactive network template is jointly funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy and the DOE Office of Electricity. In late 2015, one of the first projects to be funded by the DOE Grid Laboratory Modernization Laboratory Consortium was the Clean Energy and Transactive Campus project, led by Pacific Northwest National Laboratory. DOE funds were matched by an investment by the Washington Department of Commerce through its Clean Energy Fund. The transactive network template was developed to guide the implementation of transactive energy networks within this project’s scope.

24 POWER TRANSMISSION AND DISTRIBUTION↗