Enhancing the Capacity for Information Assurance Education Through Interdisciplinary Collaboration
This project is increasing Rutgers University's capacity to produce highly trained information assurance (IA) professionals by developing new interdisciplinary degree programs at both the graduate and undergraduate levels. A unique aspect of the effort is that it addresses the dependability of the information and information services, as well as the big data and cloud computing infrastructure, in an integrated manner. Specifically, the investigators are developing three new degree tracks: (1) A graduate-level interdisciplinary concentration in IA is being created by leveraging the recent creation of a Professional Science Master's Program that offers a Master of Business and Science (MBS) degree. Because it includes business and management training as well as technical training, the MBS degree is particularly well-suited for training current and future professionals to ensure that information assurance is considered at all stages of IT system development and deployment. (2) An IA track in the MS program offered by the Computer Science Department is being created. This track allows students looking for a traditional CS degree to receive an in-depth education in IA and to obtain a degree that recognizes this specialized training. (3) IA tracks in the BS programs offered by the Industrial and Systems Engineering (ISE) and Management Science and Information Systems (MSIS) departments are being created. These degrees are well-grounded in their individual disciplines but also expose students to IA courses from other departments to ensure that the students develop a broad interdisciplinary perspective on IA. The investigators are organizing several workshops to disseminate ideas and results of the curriculum-development activities. They are also hosting a summer camp in IA for high school students, which provides additional outreach as well as a recruitment opportunity.
EARS: Collaborative Research: Big Bandwidth: Finding Anomalous Needles in the Spectrum Haystack
The objective of the proposed project is to explore the problem of scanning large amounts of spectrum in order to detect anomalous usage of that spectrum. The project will examine spectrum scanning using a single spectrum sensor and using multiple spectrum sensors. The approach will involve using game theoretic formulations that allow for the determination of scanning strategies that give an optimal likelihood of detecting an adversarial or accidental misuse of spectrum in terms of the bandwidth that can be scanned in a single scan and the bandwidth that an anomalous activity might involve. The optimization of strategies are complemented by techniques that increase the amount of spectrum that can be scanned in a single scan, and spectrum mapping algorithms that estimate the received power levels at arbitrary spatial locations. Intellectual merit: The intellectual merit of the proposed effort stems from the pulling together of a mixture of technologies from different fields, including game theory, signal processing, security, wireless communications, and RF photonics to address the challenging problem of detecting and preventing anomalous spectrum activity across a wide swath of bandwidth. Broader impacts: The broader impacts of the proposed effort will include the cross-pollination between different disciplines, such as game theory, security, photonics and signal processing. Additionally, the project will guide the development of graduate and undergraduate students at both participating institutions, giving the students new tools with which to contribute to wireless and optical communications. Finally, new interdisciplinary curricula will be developed as part of the effort.
Rebecca N. Wright
REU Site: DIMACS REU in Computing Theory and Multidisciplinary Applications
This funding renews a highly-successful CISE Research Experiences for Undergraduates (REU) site at the Center for Discrete Mathematics and Theoretical Computer Science, headquartered at Rutgers University (DIMACS). The site focuses on research in theoretical computer science, discrete mathematics, and their multidisciplinary applications. It exposes students to a broad range of Computer Science topics, applied in contexts that range from bioinformatics to big data. The site uses mentoring teams to support interdisciplinary research projects. The site has a long-standing collaboration with The DIMATIA Center at Charles University in the Czech Republic, and five students from the Czech Republic will participate in the first 7 weeks of the program each summer. This REU site will allow 10 undergraduate students per year from the United States to experience research in theoretical computer science, discrete mathematics, and their application to multidisciplinary research. These students will be joined by five Czech students each year and will have the opportunity to participate in the full range of scientific activities at DIMACS. The program aims to influence the choices about further education and future careers of the students involved, and to give them the confidence to pursue these choices. The site has a strong record of recruiting students from underrepresented groups and of publication of research results by the participating students. This site is co-funded by the Department of Defense in partnership with the NSF REU program.
Scalable Data Coupling Abstraction for Data-Intensive Simulation Workflows
A Scalable Data Management Abstraction for Large-scale Coupled Simulation Workflows Coupled scientific simulation workflows, integrating multiple physics and scales and running at very large scales on high-end resources, have the potential for achieving unprecedented levels of accuracy and providing dramatic insights into complex phenomena. However, the coupled component of these simulation workflows need to interact and exchange significant amounts of data at runtime, and the data often has to be transformed as it flows from source to destination. As the volumes and generation rates of this data grow, the costs (latencies and energy) associated with extracting this data and transporting it for coupling, transformation and analysis have become the dominating overheads and are dictating the level of performance and productivity that can be achieved. The goal of this project is to address these challenges and to develop conceptual solutions as well as a software framework that can enable the large-scale data-intensive simulations. Our approach is based on the premise that given the large data volumes and associated costs, data will have to be largely processed online, ?in-situ? and ?in-transit? while it is staged using resources within the computational platform, and the programming and runtime system must provide abstractions and mechanisms that facilitate such data processing. Our effort is organized around three key research thrusts: (1) Programming abstractions for in-situ/in-transit data management; (2) Design and implementation of a scalable data staging substrate; and (3) Data-centric mapping and scheduling. Data and compute intensive simulations are becoming increasingly critical to a wide range of science and engineering domains, and as a result, this research has the potential to drive research and innovations in these domains. The developed framework and benchmarks also provide computer scientists with a substrate to experiment with and explore data-centric research. The development of human resources, including the training of students, researchers and software professions, as well as outreach to minorities and underrepresented group, is integral to all aspects of this effort.
Thu D. Nguyen
CSR: Small: Scheduling Energy Consumption in Green Datacenters
The massive energy consumption of today?s datacenters translates into high monetary and environmental costs; the latter because most of the electricity produced in the US comes from burning coal, a greenhouse-gas-intensive approach for producing energy. We refer to such energy as ?brown?, as opposed to the ?green? energy produced by clean energy sources. An increasingly popular approach for reducing both costs is for datacenters to generate their own green energy or draw power directly from a nearby green energy plant. In light of this trend, the goal of this project is to study how best to exploit solar and wind energy for lowering energy costs and brown energy consumption in datacenters. The major challenge in using these types of green energy is that they are not always available. In this context, our research focuses on: (1) Characterizing and modeling datacenter workloads and green energy production; (2) Designing load scheduling and energy usage policies; (3) Designing energy management policies that account for green energy; and (4) Designing systems that leverage our models and policies. Our project will impact society in many ways, including: (1) reducing the brown energy consumption and the carbon footprint of datacenters; (2) promoting the generation and consumption of green energy; and (3) creating the machinery needed to exploit green energy in datacenters for highest bene?t. Furthermore, we believe that undertaking work with such clearly de?ned societal impact will help us attract and train a diverse and committed set of undergraduate and graduate students.