Michael Zink - US grants

Our ability to monitor, anticipate, and respond to changing circumstances and events is increasingly
important, particularly with regard to our physical surroundings. Nowhere is this capability more vital to
society, or the challenges associated with its practical implementation greater, than in the context of the
atmosphere, where hazardous local weather, such as thunderstorms, tornadoes, microbursts, snow storms,
and floods as well as lofted radiological, chemical and biological agents can, in a matter of minutes or
hours, destroy or contaminate life and property over vast areas. Yet, the portion of the atmosphere that
contains the bulk of both natural and man-made hazards the lower troposphere and particularly the
atmospheric boundary layer is grossly undersampled by today's sensing technologies.
Our ERC proposes a revolutionary new paradigm in which transforming systems of distributed,
collaborative, and adaptive sensing (DCAS) networks are deployed to overcome fundamental
limitations of current approaches. Here, distributed refers to the use of large numbers of
appropriately spaced sensors capable of high spatial and temporal resolution throughout the entire troposphere.
These systems will operate collaboratively within a dynamic information technology infrastructure,
adapting to changing conditions in a manner that meets competing end user needs. These systems will
achieve breakthrough improvements in sensitivity and resolution leading to significant reductions in tornado
false alarms, vastly improved precipitation estimates for flood prediction, fine scale wind field imaging
and thermodynamic state estimation for use in airborne hazard dispersion prediction and other applications.
Successful implementation of DCAS systems will require fundamental breakthroughs consistent with the
NSF Technical Merit Review Criteria. Among these breakthroughs will be integration and sharing of knowledge
across disciplines; design and fabrication of low cost, multi beam, solid state radars; creation of a systems
based architecture to organize sensing, computing, and communications resources; development of
twoway end user interfaces that dynamically target system resources; deployment of integrative test beds to
validate assumptions and understand emergent system behavior; implementation of cross linked hierarchical
data storage and processing; and improved understanding of small scale atmospheric processes.
To achieve these breakthroughs, we have assembled leading engineering and computer science
experts from the University of Massachusetts, Amherst. They will work in partnership with scientists and
engineers from the University of Oklahoma, Colorado State University and the University of Puerto Rico,
Mayaguez, and corporate partners including Raytheon, IBM, Vaisala, and federal and state government
agencies to create the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). We will create
scalable prototype test beds to demonstrate the potential for DCAS to revolutionize our understanding,
detection, and prediction of hazardous atmospheric phenomena with end users involved from the outset.
CASA meets the NSF Broader Impacts Review Criteria through: comprehensive education and outreach
programs that introduce systems based engineering to K-12 students via the mandated engineering/technology curriculum in Massachusetts, and serves as the mechanism for expanding participation by under represented groups in engineering and scientific endeavors at all levels. Further, it will engage first responders and other end users through the provision of both technology and training. CASA will address the observation, prediction and response of weather, an issue that affects between 10 percent and 30 percent of the U.S. gross national product.
Our management structure has the flexibility to take advantage of our broad partnership. For example,
CASA will collaborate with industry partners, who, in turn, will create new product lines and services
based on our new paradigm for sensing, analyzing, predicting and responding to atmospheric hazards in
the troposphere.

Project Summary
The Collaborative Adaptive Sensing of the Atmosphere (CASA) Center is one of the 15 currently funded NSF Engineering Research Centers. CASA was established in 2003 with a vision to revolutionize our ability to observe, understand, predict and respond to weather hazards. We proposed to realize this vision by creating Distributed Collaborative Adaptive Sensing (DCAS) networks that sample the atmosphere where and when end-user needs are greatest. Since 2003 CASA has made significant contributions to basic research, technology innovation and system integration and has begun to demonstrate the power of the proposed concept. One of the major accomplishments of CASA is the design, construction, deployment and operation of the IP1 test bed in Oklahoma. This prototype 4-node radar network is allowing us to test our hypotheses and validate our results. Research is progressing on all fronts and so is the further development and expansion of this prototype network. Our hope is that our work will pave the way for industry and the government to adopt the concept and install a large number of interconnected networks of small radars across the nation, coast-to-coast, providing unprecedented ability to observe, forecast, warn, and respond to hazardous weather events.

Our research and development plan going forward is described in detail in the 2009 Annual Report. Industry has already played a fundamental role in the establishment and evolution of CASA with industrial partners and the Industry Advisory Board supporting our activities in a variety of ways. This special funding opportunity will allow us to engage industry in more direct, effective and innovative ways in the CASA enterprise. In particular, it will give us the opportunity to bring industrial personnel on the campuses to work closely with academic researchers and students on research projects and our test beds. It will also allow us to bring industrial processes and practices into the classroom. We propose to hire a full-time Research Engineer and several part-time Professors of Practice and Innovation as described below:

Research Engineer: Hire a Research Engineer who will work as part of the CASA team to enhance the future development and expansion of our IP1 test bed. This will enable the direct transfer of modern industrial practices and knowhow into CASA designs and operations.

Professors of Practice and Innovation: Hire several industry experts who can: 1) educate academic researchers and students on how to successfully transfer technology to the marketplace; 2) provide instruction in state-of-the-art industrial principles and practices that support CASA research and educational activities in systems engineering; and 3) help establish systems engineering academic offerings.

Intellectual Merit: The addition of the Research and Engineer and the Professors of Practice and Innovation will play a vital role in both CASA's research and educational activities. The close interaction of experienced industry personnel with faculty and students will improve the development of our test beds, enhance the development of our academic offerings and assist with the translation of our prototype test beds for government and industry use.

Broader Impacts: CASA is poised to make fundamental contributions to the observation, forecasting, warning and response to hazardous weather events. This work can potentially have a very significant positive impact on society by protecting lives, minimizing injuries and improving the quality of life.

This project demonstrates the benefits of connecting radars to ultra high-speed networks to improve hazardous weather warning and response and the identification and tracking of small, low-flying aircraft by developing new detection algorithms that operate directly on uncompressed, high-bandwidth radar data. Although radar networks are a critical part of the nation's infrastructure for weather observation and aircraft surveillance, today's best-effort Internet is used to transport data from radars to a variety of end users for decision-making. To allow for real-time delivery, the radar data are compressed and important information can be lost during this process. Transmitting uncompressed radar data over ultra high-speed networks could enable advanced, very geographically precise detection and prediction of weather hazards resulting in benefits to public safety and the economy. In addition, the ability to track, small low-flying aircraft is important for drug enforcement and for tracking Unmanned Aircraft Systems used for homeland security and law enforcement that are expected to be prevalent in urban areas in the near future. The project will be conducted using a test bed of high resolution, low-cost radar located in the Dallas Fort Worth Metroplex linked to users such as emergency managers and National Weather Service forecasters. To manage the increased demand for processing and networking resources from the usage of high-bandwidth data techniques related to the usage of on-demand networks (SDN) and compute resources (cloud computing) are studied.

Multi-Wave deploys new cyber-physical infrastructure on the campus of the University of Massachusetts Amherst, at the Massachusetts High-Performance Computing Center (MGHPCC), and at two collocation sites (1 Federal Street, Springfield, MA and 300 Bent Street, Cambridge, MA) to build a dedicated multi-lambda transport ring between the sites and to provide researchers with a dedicated high-bandwidth network. This new infrastructure allows research traffic to be isolated from the mix of other information by dedicating one of two wavelengths for the research network only. All other traffic is transported on the other wavelength. This new cyber-physical infrastructure is supporting research in the areas of big data and genomics, remote sensing, biostatics, planetary science, distributed systems and software defined networking, and climate science. Besides offering a reliable, high-bandwidth transport ring for researchers in computational sciences, the new cyberinfrastructure is also designed to integrate well into the existing, nation-wide Global Environment for Network Innovations (GENI) testbed.

This project will significantly impact researchers at UMass Amherst who are dealing with very large data sets. The new cyber-physical infrastructure will allow these researchers to transport large data sets between sources on and off campus to the MGHPCC, where significant computing resources are housed. For example, genomics researchers will be able to transmit very high volumes of data from storage systems at a sequencing center to the computing cluster at MGHPCC, instead of physically shipping hard drives via express mail as they do now.

This multi-institution project will design, develop, demonstrate and evaluate next-generation, resilient warning systems for rapid-onset hazards, such as tornadoes and flash floods. Using high spatio-temporal observations and short-term forecasts of lower-atmospheric conditions, this system will deliver user-centric, context-aware forecasts and warnings resulting in significant improvements in public response. Warning systems help mitigate the negative socio-economic impacts of natural hazards such as floods and tornados, which over the past three years caused 2303 injuries, 297 deaths and $8.5 billion in property and crop damages across the United States. As technology and its application evolve, and as our understanding of complex interactions among natural hazards, technology and human behavior improves, warnings must also evolve and change. This effort will develop a systems-level framework and underlying technology, firmly grounded in an understanding of human behavioral response, to leverage these changes to better meet the ultimate goal of improving safety for both people and property.

The intellectual merit of this effort centers upon development of new techniques for real-time, high-resolution nowcasts for rain and wind; creation of a new context-sensitive communication architecture for efficiently disseminating user-specific information tailored to various population segments; development and testing of new measures of human behavioral response; creation of a deeper of understanding of myriad influences on public response to weather hazards and warnings; and identification of new ways to link the social and technical components of the warning system. Research results will be integrated into the warning system based on time, space and risk considerations and demonstrated via prototyping next generation warning system concepts. Three key trends will be addressed: i) The rapid increase in ownership of mobile phones in all segments of the population; ii) New high spatiotemporal resolution X-band radar nowcasts (0- 1 hour) that can localize hazard risk and enable operational forecasters and emergency mangers to warn on a neighborhood-scale for tornados and flash floods; iii) New concepts for resilient data-dissemination architectures that enable targeting of weather information by context, such as precise location, or by demographics such as age. Live warning system experiments in the Dallas Fort Worth Metroplex will provide a unique opportunity to conduct empirical research, validate new technology and theoretical concepts. During severe weather events, high-resolution radar products will be disseminated to NWS forecasters who in turn will disseminate experimental geo-targeted, context-aware real-time warnings to individuals via mobile phones equipped with an app that logs information-seeking activity, communications, location, and movement, and enables post event surveys. Our project leverages the stakeholder partnerships, technology, and socio-technical research practices of the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) and the GENI infrastructure that enables at-scale research in next-generation networks and applications.

Broader impacts of this effort will ultimately include improved public safety and disaster mitigation and potential for applications of developed technology well beyond the Dallas-Fort Worth region.. An advisory board which includes representatives of the North Central Texas Council of Governments, the City of Fort Worth, the National Weather Service Office of Science and Technology and others will enable faster translation of our results into products and services that can be replicated in north Texas, and other parts of the nation. Research on public response addresses an underserved population through our analysis of the Accessible Hazard Alert System for people with visual or hearing impairments. The K-12 education efforts, directed at minority serving institutions, include the creation of a weather programs on safety, informed by our public response research.

The goal of this project is to demonstrate new cyber-physical architectures that allow the sharing of closed-loop sensor networks among multiple applications through the dynamic allocation of sensing, networking, and computing resources. The sharing of sensor network infrastructures makes the provision of data more cost efficient and leads to virtual private sensor network (VPSN) architectures that can dramatically increase the number of sensor networks available for public use. These cyber infrastructures support a paradigm, called Sensing as a Service, in which users can obtain sensing and computational resources to generate the required data for their sensing applications. The challenge in sharing closed-loop sensor networks is that one application's actuation request might interfere with another's request. To address this challenge the VPSN architectures are comprised of three components: 1) a sensor virtualization layer that ensures that users obtain timely access to sensor data when requested and isolates their requests from others' through the creation of appropriate scheduling algorithms; 2) a computation virtualization layer that enables the allocation of computational resources for real-time data intensive applications which is closely tied to the sensor virtualization layer; 3) a virtualization toolkit that supports application developers in their efforts to build applications for virtualized, closed-loop sensor networks.

The sharing of closed-loop sensor networks leads to substantial savings on infrastructure and maintenance costs. The proposed VPSN architectures enable users to create their own applications without having detailed knowledge of sensing technologies and allows them to focus on the development of applications. VPSNs will contribute to the creation of a nationwide, shared sensing cyber infrastructure, which will provide critical information for public safety and security. VPSNs will also help to revolutionize the way undergraduate and graduate students from many disciplines perform research. Students will be shielded from some of the complexities of sensor networks and allowed to focus on their core research. To prepare students from the Electrical and Computer Engineering (ECE) department at the University of Massachusetts to perform this kind of research, new classes in the area of Integrative Systems Engineering and Sensor Network Virtualization will be offered.

Many cities face tremendous water-related challenges due to urban population growth and climate fluctuations. Even moderate rainfall can quickly fill and overflow urban water reserves. Urban areas are particularly susceptible not only to excesses and shortages of water but also to variations in water quality. This project protects urban areas from the shocks of extreme precipitation cycles and urbanization by advancing our understanding of the urban water cycle through the integration of advanced computing and cyber-infrastructure, environmental modeling, geoscience, and information science.
This project utilizes high-resolution precipitation information from the network of Collaborative Adaptive Sensing of the Atmosphere (CASA) radars available in the Dallas-Fort Worth area, crowdsourced water observations for ubiquitous sensing of surface water over a large urban area, and new innovative wireless sensors for water quantity, water quality and soil moisture to close the observation gaps. Cloud computing is then used for advanced high-resolution modeling, data optimization, and predictive analytics to assess water quantity and quality in both the short and long term. This project advances our understanding of urban sustainability and the associated challenges through environmental, social and economic responses of a large city as an uncertain dynamic system.

Many of the ideas that drive modern cloud computing, such as server virtualization, network slicing, and robust distributed storage, arose from the research community. But because today's clouds have particular, non-malleable implementations of these ideas "baked in," they are unsuitable as facilities in which to conduct research on future cloud architectures. This project creates CloudLab, a facility that will enable fundamental advances in cloud architecture. CloudLab will not be a cloud; CloudLab will be large-scale, distributed scientific infrastructure on top of which many different clouds can be built. It will support thousands of researchers and run hundreds of different, experimental clouds simultaneously. The Phase I CloudLab deployment will provide data centers at Clemson (with Dell equipment), Utah (HP), and Wisconsin (Cisco), with each industrial partner collaborating to explore next-generation ideas for cloud architectures

CloudLab will be a place where researchers can try out ideas using any cloud software stack they can imagine. It will accomplish this by running at a layer below cloud infrastructure: it will provide isolated, bare-metal access to a set of resources that researchers can use to bring up their own clouds. These clouds may run instances of today's popular stacks, modest modifications to them, or something entirely new. CloudLab will not be tied to any particular particular cloud stack, and will support experimentation on multiple in parallel.

The impact of cloud computing outside the field of computer science has been substantial: it has enabled a new generation of applications and services with direct impacts on society at large. CloudLab is positioned to have an immediate and substantial impact on the research community by providing access to the resources it needs to shape the future of clouds. Cloud architecture research, enabled by CloudLab, will empower a new generation of applications and services which will bring direct benefit to the public in areas of national priority such as medicine, smart grids, and natural disaster early warning and response.

The public's appetite for detailed weather information is on the rise, but current weather forecasting accuracy is not satisfactory, especially when it comes to predictions for small-scale areas (e.g., neighborhood-, or block-level). Current national radar systems provide weather information updated once every 5 minutes and the quality of the data degrades considerably with increased distance from the radar. Therefore, in case of severe weather, some events might remain unnoticed or the information about them might not be delivered on time. A next generation, short-range weather radar developed at UMass Amherst promises higher temporal and spatial resolution as well as a reliable, low-cost operation.

The new class of compact weather radar developed by this team promises much higher data resolution and can be deployed on rooftops, communications towers, and other infrastructure elements, without expensive structure modifications. The low-weight of the system allows for a 2 person installation without the usage of a crane or helicopter further reducing the overall cost. The radar radiates a peak power on the order of 100W, rather than in the range of 250-500kW - a standard for current operational weather radars. Therefore the proposed sensing system does not pose any radiation hazard. Due to its short-range nature of operation, observation of the weather close to the ground, which directly affects local communities, is possible. With the proposed technology, up-to-the-second radar information will be transmitted to individuals and organizations that make critical decisions about the weather.

Scientific instruments can produce vast amounts of data. Besides capturing and storing data, it is important to give the global research community fast access to this data. In the case of remote sensing instruments like Doppler weather radar the challenges such instruments face in terms of connecting them to high-bandwidth networks is the fact that they have to be placed outside the lab at locations that are well suited for their sensing tasks, which are often not close to high-bandwidth networking infrastructure. For example, a radar is ideally placed at a location that is free of any obstructions such that it can scan the atmosphere with as little interference as possible.

The overarching goal of this project is to improve research that focuses on remote sensing by providing researchers the ability to transmit high-bandwidth time-series or spectral data, and analog signals via radio over fiber, in real time from sensors to remote storage and computing. The project improves connectivity from an on-campus tower hosting radar and other atmospheric sensors to the UMass Amherst computer network. A fiber connection is established between the tower and the main campus network with local storage capacity increases to 8 TB at the tower. This integration permits several new modes of real time data analysis for weather system research and allows for both more data to be captured and for more timely access to data.

Improving atmospheric observations has significant impacts on weather modeling in general and weather warning and prediction in particular. Thus this project has the potential to support applications that increase the safety and security of US communities and the nation as a whole. In addition, this campus network infrastructure opens new opportunities for students. For example, students can obtain live analog or high-bandwidth time series radar data to experiment with.

This PFI: AIR Technology Translation project focuses on demonstrating the use of low-cost radar systems for micro-drone and severe weather surveillance in urban and airport settings. The Federal Aviation Administration forecasts sales of seven million drones in 2020 following recent regulations allowing routine use of drones in the national airspace. While a vast majority of drone operators will likely follow the regulations and drones will be successfully monitored, non-cooperative drones or drones that have lost communication with their operators, will pose a significant threat to airport facilities where no intrusions should be allowed on the air field, critical infrastructure such as military bases or power reactors, and stadiums and arenas where drones can disrupt activities, endanger lives and cause economic losses. These facilities also risk similar outcomes when severe weather unexpectedly disrupts operations. This project will demonstrate the feasibility of a single radar-based surveillance system that can simultaneously provide early warning of drone intrusions and impending severe weather. The development of a multi-function surveillance system with multiple applications, could help make installations economical, with the costs shared between various agencies in new public-private partnership models of infrastructure acquisition, operations and data sharing.

This project proposes the use of dual polarized phased array radars that can conduct rapid electronic scanning of the atmosphere to identify and discriminate between different hard targets such as micro-drones and birds and atmospheric targets such as hydrometeors, whose characteristics help determine the type of weather. This project will help develop an understanding of dual polarization radar signatures of various micro-drones, signal processing algorithms that help distinguish such targets from clutter in the low atmosphere, and new tracking algorithms tuned to the 'stop, hover and rapidly accelerate' movement of micro-drones. While the use of phased array radars to track severe weather has already resulted in early commercial prototypes, this project will result in design and operational strategies to conduct micro-drone and weather surveillance using the same system.

Graduate students involved in this project will benefit from direct interaction with industry stakeholders and end users such as airport managers. This includes industry mentoring and participation in commercialization activities such as patent searches, intellectual property protection, and participation in business plan competitions. Teams of undergraduate students will be involved in systems engineering activities. The proof of concept demonstration to potential investors, manufacturers and customers, will lead to the specification of user requirements, development of commercial prototypes and eventually tenders and sales that result in installations. Partners includes firms such as Raytheon Company and Scientific Systems Company, Inc. that will help guide commercialization activities and end users such as Dallas Fort Worth International Airport that will provide end user needs and validate the proof-of-concept demonstration results.

This project is jointly funded by the Division of Industrial Innovation and Partnerships and the Division of Engineering Education; reflecting the alignment of this project with the respective goals of the two divisions and their programs.

This project seeks to extend the capabilities and capacity of the Cloudlab compute cloud in the second phase of the NSFCloud program. Since CloudLab began service in mid-2015, this experimental research infrastructure has become a critical experimental resource for the computer science research community. CloudLab has served over 2,000 users in over 39,000 experiments spanning more than 400 projects, with experimenters coming from nearly every state. The Cloudlab project continues to increase its support of computing for the domain science research communities, and support of technology transfers to commercial cloud technology providers and users.

The proposed Phase II activity seeks to expand the testbed capacity and capabilities through 11 enumerated hardware and software extensions to satisfy the growing needs of leading computer science systems researchers. The three major areas of new technology investments are in 1) new cloud networking technology, 2) support for new cloud architectures, and 3) increased programmability in cloud network data planes.

Computational science today depends on many complex, data-intensive applications operating on datasets that originate from a variety of scientific instruments and data stores. A major challenge for data-driven science applications is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud infrastructures provide the building blocks to construct integrated, reconfigurable, end-to-end infrastructure that has the potential to increase scientific productivity. However, applications and workflows have seldom taken advantage of these advanced capabilities.
Dynamo will allow atmospheric scientists and hydrologists to improve short- and long-term weather forecasts, and aid the oceanographic community to improve key scientific processes like ocean-atmosphere exchange, turbulent mixing etc., both of which have direct impact on our society.

The Dynamo project will develop innovative network-centric algorithms, policies and mechanisms to enable programmable, on-demand access to high-bandwidth, configurable network paths from scientific data repositories to national CyberInfrastructure facilities, and help satisfy data, computational and storage requirements of science workflows. This will enable researchers to test new algorithms and models in real time with live streaming data, which is currently not possible in many scientific domains. Through enhanced interactions between Pegasus, the network-centric platform, and new network-aware workflow scheduling algorithms, science workflows will benefit from workflow automation and data management over dynamically provisioned infrastructure. The system will transparently map application-level, network Quality of Service expectations to actions on programmable software defined infrastructure.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

360 video is a form of virtual reality (VR) that allows the viewer to experience media content in an immersive fashion. In contrast to traditional video, 360 video is recorded with a special camera that captures the complete surroundings from almost all directions. Viewers consuming such a video can select the direction they are looking at by using a pointing device on a regular display or through head movement using a head-mounted device. This new format allows a viewer to change their viewing direction when watching the video, e.g., a viewer can watch a sporting event from multiple perspectives on the field. However, creating, storing, and disseminating 360 videos at a large scale over the Internet poses significant challenges. These challenges are the focus of this project, which will develop a new system and framework, called mi360World, to enable smooth delivery of and interaction with 360 video by any user on the Internet. This project, if successful, will significantly improve 360 video delivery, and will enable new and much richer educational, training and entertainment experiences. It will also help train a new class of multimedia systems researchers and practitioners.

The mechanisms for delivering a high-quality, personalized 360 video over the Internet to a globally distributed set of users is an unsolved scientific problem that entails the following challenges: 1) Ultra-high Bandwidth; 2) Ultra-low Delay; 3) View Adaptation (to user head movement); 4) Complex video metadata and delivery; 5) Video Quality of Experience (QoE). Traditional video QoE has seen extensive research over the years; however, what contributes to 360 video QoE is much less understood and will require conceiving of and measuring new metrics. The proposed mi360World system incorporates three major research thrusts to address the above challenges: A video creation thrust that enables personalized viewing by generating navigation graphs and cinematographic rules, while maintaining a high QoE and reducing cybersickness. The construction of navigation graphs and inclusion of cinematographic rules represent the main innovations of this project, and are encapsulated in a three-layered metadata representation of the 360 video: a transport layer, a semantic layer, and an interactive story-telling layer. The second thrust focuses on scalable distribution of 360 videos to a global set of diverse viewers, utilizing navigation graphs and cinematographic rules for highly efficient transition-predictive prefetching and caching. The third thrust focuses on QoE and has the goal of devising novel QoE metrics and evaluation methods to assess cybersickness. System architectures and algorithms will be extensively evaluated through simulation, emulation, and benchmarking using testbeds to assess the success of the proposed research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Unmanned Aerial Vehicles (also known as drones) are becoming popular in the sky. Their application reaches from hobby drones for leisurely activities to life-critical drones for organ transport to commercial applications such as air taxis. The safe, efficient, and economic operation of such drones poses a variety of challenges that have to be addressed by the science community. For example, drones need very detailed, close to the ground weather information for safe operations, and data processing and energy consumption of drones need to be intelligently handled. This project will provide tools that will allow researchers and drone application developers to address operational drone challenges by using advanced computer and network technologies.

This project will provide an architecture and tools that will enable scientists to include edge computing devices in their computational workflows. This capability is critical for low latency and ultra-low latency applications like drone video analytics and route planning for drones. The proposed work will include four major tasks. First, cutting edge network and compute infrastructure will be integrated into the overall architecture to make them available as part of scientific workflows. Second, in-network processing at the network edge and core will be made available through new programming abstractions. Third, enhanced end-to-end monitoring capabilities will be offered. Finally, the architecture will leverage the Pegasus Workflow Management System to integrate in-network and edge processing capabilities.

Providing best practices and tools that enable the use of advanced cyberinfrastructure for scientific workflows will have a broad impact on society in the long term. The science drivers that will be supported by this project have the potential to increase the safety and efficiency of drone applications, an area that will grow in significance in the foreseeable future. The project team will enable access to a rich set of resources for researchers and educators from a diverse set of institutions (historically black colleges and universities (HBCU), community colleges, women?s colleges) to further democratize research. In addition, collaboration with the NSF REU (Research Experience for Undergraduates) Site in Consumer Networking will promote participation of under-served/under-represented students in project activities.

Information about the project will be available at http://www.flynet-ci.org to provide information on overall project activities, outreach activities, publications, tools and software, and the project?s team members. The project website will be preserved for at least three years after the project ends.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Scientific research is increasingly performed "in silico", which means experimentation is performed by a computer instead of using traditional laboratories. Chemistry, Biology, and Physics are only a small set of examples of the science domains that heavily rely on scientific computing to make new discoveries. Many of the computational models these discoveries are based on need massive amounts of computational, networking, and storage resources. The goal of this project is to make a new type of processing unit --Field Programmable Gate Array (FPGA)-- available to the research community to offer new and efficient ways of computation and data communication.

This collaborative project, which brings together researchers from Northeastern University and the University of Massachusetts Amherst, will provide tools and tutorials to allow network researchers to use the P4 programming language in network-attached FPGAs that are available in the FABRIC, Chameleon, CloudLab, and Open Cloud Testbed testbeds supported by NSF. Network-attached FPGAs support the disaggregation of resources such as memory, network and processing, and novel tools will be investigated to support this more general cloud processing model. Once established the tools provided by this project will enable innovative research into i) network security to ensure the security of systems where a user can generate arbitrary networking traffic; ii) in-network telemetry; iii) in-network processing; and areas not yet envisioned by the proposers.

Scientific computing has supported many discoveries in the past decade (e.g., gravitational waves, black holes, protein folding) and will lead to many more in the future, with many of them having broad societal impacts. This project will generate tools that will make the use of network and compute resources more efficient to better support scientific applications. P4 research projects will be developed as samples to be used by the broader community. In addition, the project will support two graduate students who will develop important skills in data center disaggregation and advanced networking. Tools and tutorials developed as part of this project will be used in data center and networking classes the principle investigators teach.

All tools developed will be open sourced so that any researcher can use and expand on them. Documentation and tutorials will be developed and shared to aid widespread adoption. A website with links to code, tutorials, and results will be hosted on the Open Cloud Testbed website (https://octestbed.org/). This information will be maintained for a minimum of three years after the end of the project.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The COVID-19 pandemic has greatly reinforced the need for virtual meetings in both work-related and social settings. It has also highlighted the dire lack of video conferencing tools that can simulate the rich immersive experience of in-person meetings since the current tools often leading to fatigue caused by having to interact over a non-ideal communication medium. This collaborative project brings together investigators from University of Illinois, Urbana-Champaign, University of Massachusetts at Amherst, and New Jersey Institute of Technology to research, build, and evaluate a distributed system, called miVirtualSeat, that more closely simulates the immersive experience of in-person meetings, including physical and virtual participants in a physical meeting space.

The project is focused on key research challenges in providing an immersive meeting experience where physical and virtual participants interact with each other in a physical meeting room. Some participants are virtually present in the physical meeting room, but physically located in remote sites with only limited compute and network resources. The challenges are (a) detecting, tracking, and localizing distributed physical and virtual 360-degree avatars and objects in the joint immersive scene in real-time, (b) reducing the bandwidth and latency of delivering integrated and synchronized 360-degree, volumetric, and 2D/3D video, and ambisonics audio, and (c) ensuring good quality-of-experience in the form of natural interaction between physical and virtual participants.

The project addresses an immediate and important need for a post-pandemic society to enable immersive hybrid meetings that arise in the context of classrooms, conferences, office meetings, and social gatherings. miVirtualSeat will enable these meetings with a physical meeting room and remote sites situated at each of the investigators’ institutions. The outcome of the project will be new undergraduate and graduate courses in the emerging field of advanced mixed reality immersive environments. Through outreach activities, the project members will showcase miVirtualSeat and expose the broader public to the capabilities of distributed AR/VR systems.

Information and results from this project will be maintained on the project website that is available to the public at https://monet.cs.illinois.edu/miVirtualSeat/. These will include publications, links to software repositories, media releases, and links to system data repositories derived from the project.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

COllaborative Next-generation Technology In the Northeast: the UMassUnity Machine (CONTINUUM) extends an existing computational resource, UMass Amherst’s UNITY cluster, with eight novel servers that support cutting-edge IBM POWER, GPGPU and FPGA architectures. This extension enables key computational efforts at UMass Amherst and Dartmouth campuses, and the University of Rhode Island in numerous important science areas, including gravitational wave science, metagenomics, earthquake detection, modeling cancer and others. Based on the science requirements, the existing Unity cluster is extended with eight Power9 nodes, each with dual 2.7 GHz 16-core processors, 256 GB memory, accelerated by dual Nvidia Tesla V100 GPGPUs and a Xilinx Alveo U50 FPGA. Beyond the initial science drivers proposed, the new servers support many more collaborative research teams on the three campuses. Furthermore, CONTINUUM enables scientific development for the entire Open Science Grid (OSG) community, with access to 20% of the compute time.

The new servers also serve as an important tool for training undergraduate and graduate students in the efficient use of cutting-edge HPC systems. In addition, CONTINUUM supports research and education in an EPSCoR state (RI) and a university campus in Massachusetts with 38% students of color. To promote collaborations with researchers in the wider region, CONTINUUM personnel participate in the regional HPC Day conference and collaborate with the Northeast and CAREERS Cyberteams communities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

While the growth of cloud platforms has fueled the rise of a diverse set of online services in recent decades, it has also led to increasing energy consumption and, hence, carbon emissions. Cloud platforms are well-positioned to reduce their carbon emissions by transitioning to cleaner energy sources because cloud applications often have significant spatial, temporal, and performance flexibility, enabling them to shift the location, time, and intensity of their execution to better align with the availability of carbon-free renewable energy or low-carbon grid energy. Unfortunately, researchers cannot leverage this unique combination of advantages to experiment with and optimize cloud applications' carbon-efficiency because current cloud platforms do not expose energy's carbon characteristics to them. To address the problem, this project will design and implement a shared community testbed for experimenting with the design of carbon-efficient cloud applications. The testbed will be deployed in an edge data center with a local energy system that includes a co-located solar array, batteries, and cooling system. The testbed software will virtualize the energy system by exposing software-defined visibility and control of it to cloud applications, which will enable experimentation with a rich, but unexplored, design space for developing novel carbon-efficient cloud applications capable of responding to clean energy and carbon dynamics.<br/><br/>The project has the potential for significant technical impact in advancing the design of carbon-efficient cloud applications, which is important for reducing environmental damage associated with cloud platforms' increasing energy usage and carbon footprint. The project will involve significant community outreach, including annual workshops and tutorials, to both raise awareness of the importance of optimizing for carbon-efficiency and demonstrate how the testbed can enable and advance carbon-efficiency research. The project also plans to conduct outreach by incorporating sustainable computing topics as part of summer programs for local middle and high school students. The project will incorporate use of the testbed into current courses on cloud computing and green computing to both get feedback on the testbed and to enrich students' learning environment. Finally, in addition to making the testbed available to the community, the project will make the software artifacts and datasets developed by the project available to the community via the UMass Trace Repository, which hosts many open datasets collected by researchers.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.