Yong Liu, Ph.D. - US grants

0519880
0519998

From the early days of the ARPAnet to today's global Internet, most research on network protocols has focused on traditional performance metrics such as delay, loss, and throughput. However, it is becoming increasingly important that a network not only provides good performance, but also do so in the face of a complex, uncertain, error-prone, and ever-changing environment. In today's networks, operating conditions may change as a result of user behavior (e.g., a shift in traffic to a newly popular Web site) or the underlying infrastructure (e.g., an equipment failure). In all such cases, the network and its operators must respond in a robust fashion, continuing to provide good performance despite changing conditions.

The need for "robust" network operation leads to a set of design considerations that the principal investigators (PIs) refer to as the "X-ities" (since they all end in "ity"): non-fragility, manageability, diagnosability, optimizability, scalability, and evolvability. Intuitively, we know that these X-ities are crucially important if we are to design and analyze robust networks and protocols. Yet, compared with standard performance metrics, these X-ities often lack theoretical foundations, quantitative frameworks, or even well-defined metrics and meaning. The goal of this project is to build a rigorous, quantitative foundation for explicitly considering the X-ities in the design and analysis of network protocols. The PIs consider a number of specific problems, broadly in the area of routing protocols, that concretely address several of the X-ities---with particular emphasis on non-fragility and manageability---and to begin to draw larger lessons from commonalities among the problems studied.

The proposed research focuses on the X-ities in the context of the routing protocols that ensure that each computer has paths through the network to send data to other computers. There are several reasons for this choice. First, routing protocols are a crucial part of the network architecture---they are the very glue that holds the disparate parts of the Internet together. Second, the X-ities of IP routing have not received significant formal attention. Third, routing protocols expose key issues of incomplete information (e.g., across networks run by different institutions) and interacting levels of control (e.g., between applications and the underlying network)---concerns that should arise in any thorough treatment of network X-ities. Finally, routing provides a compelling context in which the X-ities can be quantitatively studied. For example, we can quantify the performance trade-off between a fragile routing solution that has been optimized for narrow, well-defined operating conditions, versus a solution that will perform well of over variety of operating conditions. The contributions of the proposed research are three-fold:

A first quantitative study of X-ities: The intellectual challenges in rigorously understanding the X-ities are many. The PIs define specific metrics and develop mathematical models to quantitatively study each X-ity.

Solutions to specific problems: To make the study of the X-ities concrete, the PIs consider a set of research problems broadly in the area of routing that are of interest in their own right.

The beginnings of a foundation for studying X-ities: The PIs believe that the study of network X-ities is a crucially important area for long-term research in networking.

The X-ity research will lead to a deeper quantitative understanding of how to develop robust network architectures and protocols---technology that is playing an increasingly crucial role in our daily lives. The broader impacts of the research will include enhanced teaching, training, and learning for our students, development and dissemination of new educational materials, and dissemination of X-ity research results throughout the technical community.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Although there are several large-scale industrial deployments of peer-to-peer (P2P) live video systems, these existing systems have several fundamental performance problems, including huge channel switching delays, large playback lags, poor performance for less-popular channels, ISP unfriendliness. In these traditional systems, a peer only redistributes the video it is currently watching. In this research, the PIs are exploring a radically different approach to P2P live video streaming, View-Upload Decoupling (VUD). The main idea of VUD is to have each peer distribute one or more channels, with the assignments being made independently of what the peer is viewing. This novel approach has three major advantages over the traditional isolated-channel designs: channel-churn immunity; cross-channel multiplexing; and the enabling of structured streaming. The PIs are developing tractable analytical performance models for multi-channel P2P video streaming systems, for both VUD and traditional design approaches. The analytical results not only highlight the advantages of the VUD approach, but also provide important ``rules-of-thumb'' for the design of VUD systems. The PIs are developing dynamic VUD provisioning algorithms that are both robust with respect to channel churn and also adapt to dynamic channel popularity and flash crowds. The PIs are developing VUD provisioning, management and streaming schemes that take into account ISP locality and largely reduce the video streaming traffic imposed on ISP networks. The PIs and their PhD students are also developing an open-source VUD prototype.

Although P2P has proven itself as a viable architectural paradigm for distributed applications, the success of future P2P applications ultimately depends on convincing users to volunteer these resources. In this project, two inter-related P2P incentive paradigms are under investigation. In the first paradigm, called Networked Asynchronous Bilateral Trading (NABT), each user has a set of online friends. Users can trade asynchronously with direct friends using local currency and a debt limits. NABT also allows trades to pass through intermediaries. For file sharing, NABT is almost as efficient as a perfect economy, where all users can trade directly with each other. Research is also aimed at extending NABT, developing designs and theories for heterogeneous P2P resource markets. The second paradigm, called Closed P2P Communities, uses a lightweight centralized banking infrastructure. Here the focus is on designing and analyzing a new class of powerful, but lightweight currency-based incentive schemes. Game theory is being applied to design incentive mechanisms that optimize deal throughput for diverse P2P markets and pricing theory is used to study the pricing dynamics in closed P2P communities. Novel approaches for detecting and evicting colluders are also under development. Prototypes for both incentive paradigms will be constructed. The project will provide a framework for a new P2P computing paradigm. It will involve undergraduate and graduate students, especially minority students. Interactions with industry will be facilitated through the CATT center at NYU-Poly. Educational material will be developed and disseminated for undergraduate and graduate level networking courses taught by the PIs at NYU-Poly.

Peer-to-Peer (P2P) technology has enabled large-scale streaming services on the Internet. The success of the next-generation P2P streaming systems largely hinges on new designs delivering a higher level of efficiency, robustness and incentives. At the same time, the increasing popularity and high-streaming rate of P2P streaming systems have already generated significant traffic stress inside ISPs' networks. The operations of future P2P streaming systems will have significant impact on the stability and efficiency of the global network infrastructure. The PI and his PhD students are developing new theory and design for the next generation P2P streaming systems. They study the service differentiation in P2P streaming to address peer heterogeneity and provide incentives.
They derive the minimum delay bounds for realtime P2P streaming and propose streaming algorithms to approach the bounds. They apply the low-delay streaming algorithms to live video streaming and 3D streaming. They develop ISP-friendly P2P streaming design to largely reduce the traffic stress imposed on ISP networks by P2P streaming applications. They also build prototypes for the proposed new designs. The research of this project will lead to open protocols, software and tools, allowing a multitude of developers, researchers, and companies to expand, improve, and evolve the next-generation P2P streaming systems. Important insights and key techniques developed in this project can also be generalized to study data streaming in general distributed systems, such as Content Delivery Networks and distributed data centers.

ABSTRACT
This EAGER award creates an interoperability test bed to identify the components of an effective layered architecture for geoscience and environmental science research. In a layered architecture, every layer consists of different technologies, each of which uses different interaction protocols. The proposed project will examine a wide variety of existing technologies in terms of their effectiveness in working across present data silos. These technologies include data grids, workflow systems, policy management systems, web visualization services, and security protocols that work with various repository catalogs. Project goals are focused on developing cyberinfrastructure tools and approaches that allow geoscience data repositories to enable new science and more effectively make their data holdings discoverable and available to the public. Essential elements of the project include the collection and comparision of various approaches and existing tools to check effectiveness in handling and integrating geoscience data, and by automating processes needed to integrate various databases and data types. The project is led by a team of experts in cyberinfrastructure and geoscience data management and employs a spiral softwar3ee development approach. Broader impacts of the work include building infrastructure for science in order to facilitate data-enabled science in the geosciences. It will also produce results that are likely to be applicable to fields outside of the geosciences. The effort supports a larger NSF effort to establish a new paradigm in the development of an integrative and interoperable data and knowledge management system for the geosciences for a new NSF initiative called EarthCube.

Virtual Reality (VR) and Augmented Reality (AR) applications are projected to be the next wave of "Killer Apps" in the future Internet. VR/AR applications facilitate vivid immersive virtual and augmented reality experience and create tremendous new opportunities in many domains, including education, business, healthcare, and entertainment, etc. Many VR/AR applications involve streaming of 360-degree video scenes. Compared with the traditional video streaming, 360-degree video streaming requires much higher network bandwidth and much lower packet delivery latency, and user's quality of experience is highly sensitive to the dynamics in both network environment and user viewing behaviors. Addressing these unique challenges, this project will develop novel 360-degree video coding and delivery solutions to enable high quality interactive, on-demand, and live video streaming.

The project includes several research thrusts to enable novel joint coding-and-delivery solutions for high quality and robust 360-degree video streaming. For interactive streaming, novel Field-of-View (FoV) adaptive coding structure will be designed to achieve low encoding and decoding latency. Realtime joint optimization of streaming rate adaption and video coding bits allocation based on the predicted FoV will be studied to maximize the rendered video quality. For on-demand streaming, a two-tier video coding and delivery framework will be developed, and the rate allocation and video chunk scheduling between the two tiers will be investigated to strike the desired balance between the rendered video quality and streaming robustness. To facilitate predictive coding and delivery, the project will develop effective algorithms for predicting user FoVs, based on the past FoV trajectory and the audio and visual content through deep learning architectures. Personalized FoV prediction based on other users' view trajectories will also be explored under the framework of recommender systems. Fully-functional 360 video streaming prototypes will be developed and tested in controlled and real network environments to validate and improve the new designs. If successful, the research will lead to new theory and designs for 360-degree video coding and help enable the wide-spread deployment of high-quality and robust 360-video streaming systems. The research findings will be made available through publications, talks, open protocols, and open-source codes, allowing a multitude of developers, researchers, and companies to evolve 360-video streaming. The project will also create valuable research opportunities for graduate and undergraduate students, especially women and minority students. Interactions with industry will be facilitated through workshops and several research centers at the New York University.

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.