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Events for April 10, 2014
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CS Colloquium: Nitin Agrawal (NEC Labs Princeton ) - Rethinking Data Abstractions for Mobile Apps
Thu, Apr 10, 2014 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nitin Agrawal, NEC Labs Princeton
Talk Title: Rethinking Data Abstractions for Mobile Apps
Series: CS Colloquium
Abstract: Mobile apps have radically changed the ways in which users store, interact, and share data. A crucial component, for building high-quality mobile apps, nowadays is the infrastructure for managing data â both locally on mobile devices and remotely through cloud-based services. In building such “data-centric” mobile apps, developers benefit from several abstractions available for local and remote I/O. In this talk, I will present evidence as to why existing data abstractions, for local storage, are counter-productive for performance, and for cloud sync, are insufficient for consistency, efficiency, and programmability. As part of our work we are rethinking the data abstractions that will empower app developers to write and deploy such apps with ease. I will present a novel data-management platform, Simba, which provides a powerful yet easy-to-use API for mobile data storage and sync. Using Simba, apps take significantly less effort to write, compared to commercially-available sync services like Dropbox, while being more efficient.
Biography: Nitin Agrawal works as a Researcher in the Storage Systems group at NEC Labs Princeton after graduating with a PhD from Wisconsin in 2009. His interests lie in distributed and mobile systems, operating systems, applied machine learning, and storage systems, and his recent research focuses on cloud infrastructure for data-centric mobile services. He has received Best Paper Awards at FAST 2009, FAST 2011, FAST 2012, and a top paper selection at FAST 2007. More details can be found at http://www.nec-labs.com/~nitin/
Host: Ramesh Govindan
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Defense - Joongheon Kim
Thu, Apr 10, 2014 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Joongheon Kim
Committee:
Andreas F. Molisch (Chair)
Ramesh Govindan (Co-chair)
Aiichrio Nakano
Antonio Ortega (Outside member)
Title: Elements of Next-Generation Wireless Video Systems: Millimeter-Wave and Device-to-Device Algorithms
Abstract:
This dissertation explores the possible issues and proposes promising solutions in next generation wireless and mobile systems.
For next generation wireless systems, one of the main research contributions is dedicated to multi-Gbps system design and implementation. To achieve multi-Gbps data rates, using millimeter-wave wireless channels is one of the most promising topics since the millimeter-wave systems can easily achieve multi-Gbps data rates according to ultra-wide bandwidth that is 2.16 Gbps in 60 GHz. Therefore, millimeter-wave technologies are actively discussing in next generation 5G cellular research in the bands of 28 GHz and 39 GHz as well. Even though the millimeter-wave wireless systems have this multi-Gbps benefit, research challenges also exist. According to the higher carrier frequencies, the attenuation of signals is a major factor that should be handled. To deal with this issue, relaying and beam training algorithms are mainly used and discussed.
For relaying in millimeter-wave wireless systems, we investigated a joint compression and relaying algorithm for outdoor video applications. Transmission of high-definition (HD) video is a promising application for millimeter-wave wireless links, since very high transmission rates are possible. In particular we consider a sports stadium broadcasting system where signals from multiple cameras are transmitted to a central location. Due to the high path-loss of 60 GHz radiation over the large distances encountered in this scenario, the use of relays might be required. The proposed algorithm analyzes the joint selection of the routes and the compression rates from the various sources for maximization of the overall video quality. We consider three different scenarios: (i) each source transmits only to one relay and the relay can receive only one data stream, and (ii) each source can transmit only to a single relay, but relays can aggregate streams from different sources and forward to the destination, and (iii) the source can split its data stream into parallel streams, which can be transmitted via different relays to the destination. For each scenario, we derive the mathematical formulations of the optimization problem and re-formulate them as convex mixed-integer programming, which can guarantee optimal solutions. Extensive simulations demonstrate that high-quality transmission is possible for at least ten cameras over distances of 300 m. Furthermore, optimization of the video quality gives results that can significantly outperform algorithms that maximize data rates.
For beam training in millimeter-wave wireless systems, we investigated a fast beam training algorithm with receive beamforming. Both IEEE standards and the academic literature have generally considered beam training protocols involving exhaustive search over all possible beam directions for both the beamforming initiator and responder. However, this operation requires a long time (and thus overhead) when the beamwidth is quite narrow such as for mm-wave beams (1 degree in the worst case). To alleviate this problem, we propose two types of adaptive beam training protocols for fixed and adaptive modulation, respectively, which take into account the unique propagation characteristics of millimeter waves. For fixed modulation, the proposed protocol allows for interactive beam training, stopping the search when a local maximum of the power angular spectrum is found that is sufficient to support the chosen modulation/coding scheme. We furthermore suggest approaches to prioritize certain directions determined from the propagation geometry, long-term statistics, etc. For adaptive modulation, the proposed protocol uses iterative multi-level beam training concepts for fast link configuration that provide an exhaustive search with significantly lower complexity. Our simulation results verify that the proposed protocol performs better than traditional exhaustive search in terms of the link configuration speed for mobile wireless service applications.
For next generation mobile systems, direct communication between mobile stations, i.e., called device-to-device communications, is actively discussed in next generation 3GPP cellular mobile systems. In addition, one of major applications of device-to-device mobile systems is adaptive video streaming. One of the most well-known device-to-device network algorithms, used in the FlashLinQ system, provides good performance in terms of the number of activated links. However, it is not optimized for transmission of video streams since it does not consider the quality, or the specific requirements of streaming. We propose an alternative algorithm that consists of a scheduling and a streaming component. The scheduling employs message-passing to determine max-independent sets. For designing the streaming component, a quality-aware stochastic algorithm is introduced that works based on the queue backlog sizes in each transmitter queue. The framework controls the quality of each chunk of video to maximize the qualities of streamed video subject to queue rate stability. The efficiencies of the proposed algorithm is verified by simulation studies in terms of (i) the number of video streaming stalls at receivers and (ii) the queue dynamics at transmitters. According to the simulation results, it is verified that the proposed algorithm presents desired performance in terms of user satisfaction and queue stability.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CS Colloquium: Aleksandra Korolova (Google) - Scalable Algorithms for Protecting User Privacy
Thu, Apr 10, 2014 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Aleksandra Korolova, Google
Talk Title: Scalable Algorithms for Protecting User Privacy
Series: CS Colloquium
Abstract: THIS TALK WILL BE BROADCAST / STREAMING VIA THE FOLLOWING LINK. (Right click-open link in new tab or window.)
Ubiquitous use of the Internet and mobile technologies combined with dropping data storage and processing costs have enabled new forms of communications and data-driven innovations. However, they have also created unprecedented challenges for privacy, with companies, policy makers, and individuals struggling in their search for approaches that could enable innovation while avoiding privacy harms.
In this talk, I will present algorithmic and data-mining research that demonstrates how these seemingly conflicting goals may be achieved, even when the data being collected about individuals is constantly changing and expanding. I will first demonstrate that merely restricting data sharing is insufficient to protect privacy via a novel attack exploiting Facebook's ad targeting system to reveal users’ secrets. I will then present algorithms that enable useful search data releases and social advertising computations while provably protecting privacy. Finally, I will show how data mining techniques used to improve web search and advertising quality can be effectively applied towards improving privacy policies and building tools for safer user experiences.
Biography: Aleksandra Korolova is a research scientist at Google, where she works on developing and implementing approaches for privacy-preserving data mining and for data-driven understanding of user privacy preferences. Aleksandra received her Ph.D. in Computer Science from Stanford, where she was a Cisco Systems Stanford Graduate Fellow. Aleksandra's thesis, "Protecting Privacy when Mining and Sharing User Data", was awarded the Arthur L. Samuel Award for the best 2011-2012 CS Ph.D. thesis at Stanford, and her work on "Privacy Violations Using Microtargeted Ads" was a co-winner of the 2011 PET Award for Outstanding Research in Privacy Enhancing Technologies. Prior to joining Google, Aleksandra has interned at PARC, Facebook, Microsoft, and Yahoo! Research. She graduated Phi Beta Kappa from MIT with a B.S. degree in Mathematics with Computer Science.
Host: Shanghua Teng
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
