Energy Sciences Network

Abstract We present one of the first case studies demonstrating the use of distributed acoustic sensing deployed on regional unlit fiber-optic telecommunication infrastructure (dark fiber) for broadband seismic monitoring of both near-surface soil properties and earthquake seismology. We recorded 7 months of passive seismic data on a 27 km section of dark fiber stretching from West Sacramento, CA to Woodland, CA, densely sampled at 2 m spacing. This dataset was processed to extract surface wave velocity information using ambient noise interferometry techniques; the resulting V S profiles were used to map both shallow structural profiles and groundwater depth, thus demonstrating that basin-scale variations in hydrological state could be resolved using this technique. The same array was utilized for detection of regional and teleseismic earthquakes and evaluated for long period response using records from the M8.1 Chiapas, Mexico 2017, Sep 8th event. The combination of these two sets of observations conclusively demonstrates that regionally extensive fiber-optic networks can effectively be utilized for a host of geoscience observation tasks at a combination of scale and resolution previously inaccessible.

Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis `on-the-fly', as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities.

The MoWiTT field facility is used to measure the convective film coefficient over the exterior surface of a window.The MoWiTT-measured data is compared to some commonly-used experimental and theoretical models.The comparison shows that the MoWiTT data disagrees with the previously used models such as the ASHRAE/DOE-2 model.The reasons for these disagreements are discussed.An experimental model, based on the Mo WiTT data, is presented to correlate the film coefficient with the difference in temperatures of the exterior glass surface and the ambient, in the natural convection region, and with the site wind speed, in the forced convection region.The wind speed is considered both in windward and leeward hemispheres.T~e validity of the MoWiTT model for low-rise buildings is then discussed.

With the advent of service sensitive applications such as remote controlled experiments, time constrained massive data transfers, and video-conferencing, it has become apparent that there is a need for the setup of dynamically provisioned, quality of service enabled virtual circuits. The ESnet on-demand secure circuits and advance reservation system (OSCARS) is a prototype service enabling advance reservation of guaranteed bandwidth secure virtual circuits. OSCARS operates within the energy sciences network (ESnet), and has provisions for interoperation with other network domains. ESnet is a high-speed network serving thousands of Department of Energy scientists and collaborators worldwide. OSCARS utilizes the Web services model and standards to implement communication with the system and between domains, and for authentication, authorization, and auditing (AAA). The management and operation of end-to-end virtual circuits within the network is done at the layer 3 network level. Multi-protocol label switching (MPLS) and the resource reservation protocol (RSVP) are used to create the virtual circuits or label switched paths (LSP's). quality of service (QoS) is used to provide bandwidth guarantees. This paper describes our experience in implementing OSCARS, collaborations with other bandwidth-reservation projects (including interdomain testing) and future work to be done.

Abstract. The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed.

BACKGROUND: Inconsistencies in outcome reporting and frequent omission of patient-centered outcomes can diminish the value of trials in treatment decision making. We identified critically important outcome domains in kidney transplantation based on the shared priorities of patients/caregivers and health professionals. METHODS: In a 3-round Delphi survey, patients/caregivers and health professionals rated the importance of outcome domains for trials in kidney transplantation on a 9-point Likert scale and provided comments. During rounds 2 and 3, participants rerated the outcomes after reviewing their own score, the distribution of the respondents' scores, and comments. We calculated the median, mean, and proportion rating 7 to 9 (critically important), and analyzed comments thematically. RESULTS: One thousand eighteen participants (461 [45%] patients/caregivers and 557 [55%] health professionals) from 79 countries completed round 1, and 779 (77%) completed round 3. The top 8 outcomes that met the consensus criteria in round 3 (mean, ≥7.5; median, ≥8; proportion, >85%) in both groups were graft loss, graft function, chronic rejection, acute rejection, mortality, infection, cancer (excluding skin), and cardiovascular disease. Compared with health professionals, patients/caregivers gave higher priority to 6 outcomes (mean difference of 0.5 or more): skin cancer, surgical complications, cognition, blood pressure, depression, and ability to work. We identified 5 themes: capacity to control and inevitability, personal relevance, debilitating repercussions, gaining awareness of risks, and addressing knowledge gaps. CONCLUSIONS: Graft complications and severe comorbidities were critically important for both stakeholder groups. These stakeholder-prioritized outcomes will inform the core outcome set to improve the consistency and relevance of trials in kidney transplantation.

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

ESnet, the Energy Sciences Network, has the mission of providing the network infrastructure to the U.S. Department of Energy's Office of Science programs and facilities, which depend on large collaborations and large-scale data sharing, enabling them to accomplish their science. ESnet4 - a hybrid IP and dynamic circuit network designed in 2006 and completed in 2008 - has managed to effectively satisfy the networking needs of the science community, easily handling dramatic growth in traffic requirements: around 80 percent growth year over year and 300 percent growth with the Large Hadron Collider (LHC) coming online. In this article, we examine the benefits and limitations of the current hybrid architecture based on actual production experience; discuss open research problems; and predict factors that will drive the evolution of hybrid networks, including advances in network technology, new computer architectures, and the onset of large-scale distributed computing.

BACKGROUND: Although advances in treatment have dramatically improved short-term graft survival and acute rejection in kidney transplant recipients, long-term graft outcomes have not substantially improved. Transplant recipients also have a considerably increased risk of cancer, cardiovascular disease, diabetes, and infection, which all contribute to appreciable morbidity and premature mortality. Many trials in kidney transplantation are short-term, frequently use unvalidated surrogate endpoints, outcomes of uncertain relevance to patients and clinicians, and do not consistently measure and report key outcomes like death, graft loss, graft function, and adverse effects of therapy. This diminishes the value of trials in supporting treatment decisions that require individual-level multiple tradeoffs between graft survival and the risk of side effects, adverse events, and mortality. The Standardized Outcomes in Nephrology-Transplantation initiative aims to develop a core outcome set for trials in kidney transplantation that is based on the shared priorities of all stakeholders. METHODS: This will include a systematic review to identify outcomes reported in randomized trials, a Delphi survey with an international multistakeholder panel (patients, caregivers, clinicians, researchers, policy makers, members from industry) to develop a consensus-based prioritized list of outcome domains and a consensus workshop to review and finalize the core outcome set for trials in kidney transplantation. CONCLUSIONS: Developing and implementing a core outcome set to be reported, at a minimum, in all kidney transplantation trials will improve the transparency, quality, and relevance of research; to enable kidney transplant recipients and their clinicians to make better-informed treatment decisions for improved patient outcomes.

BACKGROUND: The potential for clinical trials to impact patient care may be limited if the outcomes reported vary by trial and lack direct relevance to patients. Despite the many trials conducted in kidney transplantation, premature death due to cardiovascular disease, infection, and malignancy remains high. We aimed to assess the range and consistency of outcomes reported in trials in kidney transplantation. METHODS: We searched for randomized trials conducted in kidney transplantation. We extracted the outcome measures, classified them into outcome domains, and into categories (clinical, surrogate or patient-reported outcome [PRO]). We assessed the measures used for the top 4 domains. RESULTS: Overall, 397 trials reported 12 047 outcomes measures and time points (median, 19 per trial; interquartile range, 9-42) across 106 different domains, of which 55 (52%) were surrogate, 35 (33%) clinical, and 16 (15%) PRO. The 4 most frequently reported were graft function (322 [81%] trials, 118 outcome measures), acute rejection (234 [59%], 93 measures), graft loss (215 [54%], 48 measures), and mortality (204 [51%], 51 measures). The remaining 102 domains were reported in less than 50% of trials. CONCLUSIONS: Mortality- and graft-related outcome domains were frequently reported and assessed with a multiplicity of measures. Most outcome domains were surrogate outcomes, and the reporting of relevant life-threatening complications and PRO were uncommon. Establishing core outcomes based on the shared priorities of patients/caregivers and health professionals in kidney transplantation may improve the relevance and consistency of outcome reporting in trials to better inform clinical decision making.

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance data enclaves and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

We have studied the laterally seeded regrowth of polycrystalline Si film over SiO2 islands through the use of a very narrow strip electron beam of a high-energy density. A 0.5-μm polycrystalline Si film was deposited over the entire surface of wafers on which SiO2 islands, 30–100 μm wide and 2 cm long, had been formed. A stationary electron beam 60 μm×3 cm was focused on the wafer surface with an average energy density of 0.7 MW/cm2 at 10 kV. The wafers were irradiated for 0.1–2 ms while moving at a speed of up to 500 cm/s past the beam. A transmission electron microscopic analysis showed that the laterally seeded regrowth of Si films over SiO2 islands were realized successfully and that the regrowth film which covered the SiO2 islands resulted in a quasi-single-crystal film with small-angle boundaries. The average speed of the front of the lateral regrowth over the SiO2 was estimated to be about 200 cm/s.

BACKGROUND: Casein protein consumed before sleep has been suggested to offer an overnight supply of exogenous amino acids for anabolic processes. The purpose of this study was to compare supplemental casein consumed earlier in the day (DayTime, DT) versus shortly before bed (NightTime, NT) on body composition, strength, and muscle hypertrophy in response to supervised resistance training. METHODS: Thirteen males participated in a 10-week exercise and dietary intervention while receiving 35 g casein daily. Isocaloric diets provided 1.8 g protein/kg body weight. RESULTS: Both groups increased (p < 0.05) in lean soft tissue (DT Pre: 58.3 ± 10.3 kg; DT Post: 61.1 ± 11.1 kg; NT Pre: 58.3 ± 8.6 kg; NT Post: 60.3 ± 8.2 kg), cross-sectional area (CSA, DT Pre: 3.4 ± 1.5 cm2; DT Post: 4.1 ± 1.7 cm2; NT Pre: 3.3 ± 1.6 cm2; NT Post: 3.7 ± 1.6 cm2) and strength in the leg press (DT Pre: 341 ± 87.3 kg; DT Post: 421.1 ± 94.0 kg; NT Pre: 450.0 ± 180.3 kg; NT Post: 533.9 ± 155.4 kg) and bench press (DT Pre: 89.0 ± 27.0 kg; DT Post: 101.0 ± 24.0 kg; NT Pre 100.8 ± 32.4 kg; NT Post: 109.1 ± 30.4 kg) with no difference between groups in any variable (p > 0.05). CONCLUSIONS: Both NT and DT protein consumption as part of a 24-h nutrition approach are effective for increasing strength and hypertrophy. The results support the strategy of achieving specific daily protein levels versus specific timing of protein ingestion for increasing muscle mass and performance. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03352583 .

Public and Private Enterprise clouds are changing the nature of WAN data center interconnects. Data center WAN interconnects today are pre-allocated, static optical trunks of high capacity. These optical pipes carry aggregated packet traffic originating from within the data centers while routing decisions are made by devices at the data center edges. In this paper, we propose a software-defined networking enabled optical transport architecture (Transport SDN) that meshes seamlessly with the deployment of SDN within the Data Centers. The proposed programmable architecture abstracts a core transport node into a programmable virtual switch that leverages the OpenFlow protocol for control. A demonstration use-case of an OpenFlow-enabled optical virtual switch managing a small optical transport network for a big-data application is described. With appropriate extensions to OpenFlow, we discuss how the programmability and flexibility SDN brings to packet-optical data center interconnect will be substantial in solving some of the complex multi-vendor, multi-layer, multi-domain issues that hybrid cloud providers face.

There have been a lot of proposals to unify the control and management of packet and circuit networks but none have been deployed widely. In this paper, we propose a simple programmable architecture that abstracts a core transport node into a programmable virtual switch, that meshes well with the software-defined network paradigm while leveraging the OpenFlow protocol for control. A demonstration use-case of an OpenFlow-enabled optical virtual switch implementation managing a small optical transport network for big-data applications is described. With appropriate extensions to OpenFlow, we discuss how the programmability and flexibility SDN brings to packet-optical backbone networks will be substantial in solving some of the complex multi-vendor, multi-layer, multi-domain issues service providers face today.

The importance of genetic issues associated with the sourcing of propagules is increasingly recognised for successful ecological restoration. A consideration of life history traits has contributed to ‘best-guess’ scenarios on the appropriate location and desirable properties of local provenance source populations, but these can lack precision. For clonal species, population genetic structure and variation will depend on the balance between the extent and growth rate of asexual clones, sexual reproduction, pollen dispersal, and subsequent seed dispersal and recruitment. We assessed patterns of population genetic structure and variation for Alexgeorgea nitens (Nees) L. Johnston &amp; B. Briggs (Restionaceae), a dioecious, clonal, perennial species, with novel life history traits. Our results show high levels of genetic diversity within populations, and surprisingly low levels of population differentiation (FST = 0.17). We suggest that the high genetic diversity observed within these populations reflects extensive pollen dispersal and successful seeding (sexual reproduction) and recruitment events, even though direct observations of seedling recruitment are rare. In this case, a ‘best-guess’ propagule-sourcing scenario based on life-history traits that appear to limit dispersal capability does not predict the extent of high local genetic diversity and weak population genetic structure in A. nitens.

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.

Scientific applications generate large volumes of data that often needs to be moved between geographically distributed sites for collaboration or backup which has led to a significant increase in data transfer rates. As an increasing number of scientific applications are becoming sensitive to silent data corruption, end-to-end integrity verification has been proposed. It minimizes the likelihood of silent data corruption by comparing checksum of files at the source and the destination using secure hash algorithms such as MD5 and SHA1. In this paper, we investigate the robustness of existing end-to-end integrity verification approaches against silent data corruption and propose a Robust Integrity Verification Algorithm (RIVA) to enhance data integrity. Extensive experiments show that unlike existing solutions, RIVA is able to detect silent disk corruptions by invalidating file contents in page cache and reading them directly from disk. Since RIVA clears page cache and reads file contents directly from the disk, it incurs delay to execution time. However, by running transfer, cache invalidation, and checksum operations concurrently, RIVA is able to keep its overhead below 15% in most cases compared to the state-of-the-art solutions in exchange of increasing the robustness to silent data corruption. We also implemented dynamic transfer and checksum parallelism to overcome performance bottlenecks and observed more than 5x increase in RIVA's speed.

Many companies deploy multiple data centers across the globe to satisfy the dramatically increased computational demand. Wide area connectivity between such geographically distributed data centers has an important role to ensure both the quality of service, and, as bandwidths increase to 100Gbps and beyond, as an efficient way to dynamically distribute the computation. The energy cost of data transmission is dominated by the router power consumption, which is unfortunately not energy proportional. In this paper we not only quantify the performance benefits of leveraging the network to run more jobs, but also analyze its energy impact. We compare the benefits of redesigning routers to be more energy efficient to those obtained by leveraging locally available green energy as a complement to the brown energy supply. Furthermore, we design novel green energy aware routing policies for wide area traffic and compare to state-of-the-art shortest path routing algorithm. Our results indicate that using energy proportional routers powered in part by green energy along with our new routing algorithm results in 10x improvement in per router energy efficiency with 36% average increase in the number of jobs completed.