- talks - lists.cs.princeton.edu

11am Mon Dec 16 talk on integrating middleboxes into software-defined networking
by Jennifer Rexford 14 Dec '13

14 Dec '13

Speaker: Professor Minlan Yu, USC Title: Integrating Middleboxes into Software Defined Networking Date/time: 11am Mon Dec 16 Place: 302 in CS building Abstract: Networks today rely on middleboxes to provide critical performance, security, and policy compliance capabilities. Achieving these benefits and ensuring that the traffic is directed through the desired sequence of middleboxes requires significant manual effort and operator expertise. In this respect, Software-Defined Networking (SDN) offers a promising alternative. Middleboxes, however, introduce new aspects (e.g., policy composition, resource management, dynamic traffic modifications) that fall outside the purvey of traditional L2/L3 functions that SDN supports (e.g., access control or routing). In this talk, we discuss two systems that integrate middleboxes into SDN: First, we propose SIMPLE, which enforces SDN policies with unmodified middleboxes and today’s OpenFlow APIs. Second, we discuss FlowTags, which takes minimal extension of middleboxes to ensure SDN tenets (Origin binding and paths following policy). Bio: Minlan Yu is an assistant professor in the computer science department of University of Southern California. She received her B.A. in computer science and mathematics from Peking University in 2006 and her M.A. and Ph.D in computer science from Princeton University in 2008 and 2011. After that she was a postdoctoral scholar in UC Berkeley for one year. She has actively collaborated with companies such as AT&T, Microsoft, and Bell Labs. Her research interest includes data networking, distributed systems, enterprise and data center networks, network virtualization, and software-defined networking. She received ACM SIGCOMM doctoral dissertation award in 2012 and Google research award in 2013.

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11am Mon Dec 16 talk on integrating middleboxes into software-defined networking
by Jennifer Rexford 14 Dec '13

14 Dec '13

Speaker: Professor Minlan Yu, USC Title: Integrating Middleboxes into Software Defined Networking Date/time: 11am Mon Dec 16 Place: 302 in CS building Abstract: Networks today rely on middleboxes to provide critical performance, security, and policy compliance capabilities. Achieving these benefits and ensuring that the traffic is directed through the desired sequence of middleboxes requires significant manual effort and operator expertise. In this respect, Software-Defined Networking (SDN) offers a promising alternative. Middleboxes, however, introduce new aspects (e.g., policy composition, resource management, dynamic traffic modifications) that fall outside the purvey of traditional L2/L3 functions that SDN supports (e.g., access control or routing). In this talk, we discuss two systems that integrate middleboxes into SDN: First, we propose SIMPLE, which enforces SDN policies with unmodified middleboxes and today’s OpenFlow APIs. Second, we discuss FlowTags, which takes minimal extension of middleboxes to ensure SDN tenets (Origin binding and paths following policy). Bio: Minlan Yu is an assistant professor in the computer science department of University of Southern California. She received her B.A. in computer science and mathematics from Peking University in 2006 and her M.A. and Ph.D in computer science from Princeton University in 2008 and 2011. After that she was a postdoctoral scholar in UC Berkeley for one year. She has actively collaborated with companies such as AT&T, Microsoft, and Bell Labs. Her research interest includes data networking, distributed systems, enterprise and data center networks, network virtualization, and software-defined networking. She received ACM SIGCOMM doctoral dissertation award in 2012 and Google research award in 2013.

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10am Wed Dec 11 talk on pattern matching in compressed HTTP traffic
by Jennifer Rexford 11 Dec '13

11 Dec '13

Speaker: Yaron Koral, Tel Aviv University Title: Efficient multi-pattern matching on Compressed HTTP traffic Date/time: 10-10:50am Wed Dec 11 Place: 402 ’Signature-based’ detection is one of the fundamental technique to detect malicious activities in a network environment. Today, the performance of the security tools is dominated by the speed of the string-matching algorithms that detect these signatures. A significant part of the traffic over the Internet is compressed HTTP. However, current security tools do not deal with such a traffic and require some kind of decompression phase before performing the multi-patterns matching task. Thus, there is a high performance penalty in pattern matching on compressed data. In this talk, we present efficient algorithms for ’on-the-fly’ multi-pattern matching algorithms for common HTTP compression algorithms, such as GZIP and SDCH (Google's compression algorithm). Our results show that surprisingly it is usually faster to do pattern matching on the compressed data, with the penalty of decompression, than to do pattern matching on regular traffic. The talk is based on three papers: one with A. Bremler-Barr (INFOCOM 2009, later in Transactions on Networking 2012), one with Y. Afek and A. Bremler-Barr (Networking 2011, later in Computer Communication 2012) and one with S. Tzur-David, D. Hay and A. Bremler-Barr (INFOCOM 2012).

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Talk starting now in small auditorium
by Jennifer Rexford 09 Dec '13

09 Dec '13

Sent from my iPad

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Colloquium Speaker Yuanyuan Zhou
by Nicole E. Wagenblast 09 Dec '13

09 Dec '13

Do not blame users for misconfigurations Yuanyuan Zhou , University of California, San Diego Monday, December 9, 4:30pm Computer Science 105 Today's data centers usually employ high redundancy to tolerate hardware and software errors. But unfortunately another type of errors, namely configuration errors (i.e., misconfigurations) can still take down the whole data centers. It has contributed to more than 30% high severity issues and caused some of the recent major downtime in cloud service companies including Amazon, Microsoft Azure, Google, Facebook, etc. Unfortunately, many software developers and system designers put most of blames on users for configuration errors, and do not pay enough attention to handling misconfiguration in a more active way. Comparing to software bugs, configuration issues have much less tooling support for error detections, issue tracking, tolerance testing, as well as design reviews. In this talk, I will present our recent work on characterizing misconfigurations in commercial and open source systems (as well as a major commercial cloud service provider), and also some of our solutions in addressing this configuration problem. We approach it from the perspective that configurations are a part of user interface, and thereby need to consider from user perspectives. More specifically, as a practical first step, we need to avoid error-prone requirements and also react gracefully to user mistakes. Our solutions have been used two commercial companies and have influenced some popular open source systems such as "Squid" redesigning their configuration. In this talk, I will also some of the negative interactions (and "challenges") with some open source developers. Yuanyuan Zhou is currently a Qualcomm Chair Professor at UC-San Diego. Before UCSD, she was a tenured associate professor at University of Illinois at Urbana Champaign. Her research interests span the areas of operating systems, software engineering, system reliability and maintainability. She has 3 papers selected into the IEEE Micro Special Issue on Top Picks from Architecture Conferences, best paper at SOSP'05, and 2011 ACM SIGSOFT FSE (Foundation of Software Engineering) Distinguished Paper. She has co-founded two startups. Her recent startup, PatternInsight, has successfully deployed software quality assurance tools in many companies including Cisco, Juniper, Qualcomm, Motorola, Intel, EMC, Lucent, Tellabs, etc. Recently Pattern Insight has sold its Log Insight business to VmWare. In addition, Intel has licensed some of her solutions on detecting concurrency bugs. She has graduated 14 Ph.D. students so far, many of whom have joined top industrial companies and academia such as University of Wisconsin at Madison, University of Toronto, University of Waterloo, etc as tenure-track faculty.

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Fwd: Adi's talk Monday
by Moses S. Charikar 05 Dec '13

05 Dec '13

Adi Shamir will be speaking at IAS this coming Monday, Dec 9. Details below: Cheers, Moses How Cryptosystems Are REALLY Broken Computer Science/Discrete Mathematics Seminar I Topic: How Cryptosystems Are REALLY Broken Speaker: Adi Shamir Affiliation: Weizmann Institute Date: Monday, December 9 Time/Room: 11:15am - 12:15pm/S-101 Most of the cryptosystems we currently use are highly secure, and cannot be broken with reasonable complexity by mathematical cryptanalysis. However, over the last fifteen years researchers have developed many types of physical attacks on their implementations which can easily bypass their mathematical security. In this talk I will survey some of the latest techniques of this type, and show how difficult it is to build a truly secure communication systems.

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Colloquium Speaker David Malan Tues Dec 3, 4:30pm
by Nicole E. Wagenblast 02 Dec '13

02 Dec '13

Pedagogy behind CS50 David Malan , Harvard University Tuesday, December 3, 4:30pm Computer Science 105 CS50 is Harvard University's introductory course for majors and non-majors alike, a one-semester amalgam of courses generally known as CS1 and CS2. In 2007, we set out to alter the course's style and tone to resonate with those "less comfortable" and "more comfortable" alike, albeit without sacrificing the course's historical rigor. We maintained the course's underlying syllabus but revamped every problem set, providing students not only with more direction but context as well. And we augmented the course's support structure. As of 2013, CS50 is Harvard's second-largest course with nearly 700 students, up from 132 in 2006, and those "less comfortable" now compose the course's largest demographic. We present in this talk what we have done and why we have done it. And we offer a glimpse of CS50x, Harvard College's first course to be offered on an even larger scale via edX. BIO David J. Malan is a Senior Lecturer on Computer Science at Harvard University for the School of Engineering and Applied Sciences. He received his A.B., S.M., and Ph.D. in Computer Science from the same in 1999, 2004, and 2007, respectively. His research in graduate school focused primarily on cybersecurity and computer forensics. His more recent publications focus on instructional technologies and pedagogy. He now teaches Harvard College's introductory computer science course, Computer Science 50, otherwise known as CS50, which is available as OpenCourseWare at cs50.tv as well as via edx.org.

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J Lu preFPO
by Melissa M. Lawson 27 Nov '13

27 Nov '13

Jingwan (Cynthia) Lu will present her preFPO on Monday December 9 at 4:30 PM in Room 402. The members of her committee are: Adam Finkelstein, advisor; Szymon Rusinkiewicz and Stephen DiVerdi (Google), readers; Tom Funkhouser and Jianxiong Xiao, nonreaders. Everyone is invited to attend her talk. Her abstract follows below. ----- Original Message ----- Title: Data-driven Digital Drawing and Painting Abstract: Digital artists create evocative drawings and paintings using a tablet and stylus coupled with painting software like Adobe Photoshop or Corel Painter. Several research systems have shown promising improvements in various aspects of the art creation process by targeting specific drawing styles and natural media. Such systems combine carefully hand-crafted procedural rules and computationally expensive, style-specific physical simulations. Nevertheless, untrained users often find it hard to achieve their target style in these systems due to the challenge of controlling and predicting the outcome of their collective strokes. Moreover even trained digital artists are often restricted by the inherent stylistic limitations of these systems. In this thesis, I propose an alternative painting paradigm that allows novices to create artworks closely following an example style of their choice. The style is characterized by a set of example brush strokes. We present three prototype painting systems, all of which follow a common sketching interface, wherein the user's 1D paths are automatically replaced by complex 2D strokes shaped and textured by a small set of exemplars. The three prototype systems HelpingHand, RealBrush and DecoBrush make use of different types of exemplars for distinct styles of artwork. HelpingHand learns from hand gesture data to hallucinate the possible gestures used during drawing, which then drive a chosen virtual brush simulation engine to produce stylistic handwritings or line drawings. RealBrush uses photographed natural media stroke marks to stylize the user's query path, completely obviating the need for physical simulation. It also stylizes the stroke interactions based on captured physical examples. DecoBrush further generalizes the conventional "painting" concept beyond the scope of natural media to allow "painting" structured decorative vector patterns following user-sketched path, which alleviates the difficulty of creating such patterns from scratch in programs like Adobe Illustrator. All three systems address the common challenge of predicting the appearance of user's new query paths being faithful to the style of a small number of chosen exemplars. With the convenience of data sharing on the internet, this data-driven paradigm opens up new opportunities for artists and novices to create original stylistic artworks and to abstract, share, and reproduce their styles more easily and faithfully.

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S Li FPO
by Melissa M. Lawson 27 Nov '13

27 Nov '13

Shi Li will present his FPO on Monday December 9 at 3PM in Room 402. The members of his committee are: Moses Charikar, advisor; Mark Braverman and Julia Chuzhoy (TTI), readers; Bernard Chazelle and Zeev Dvir, nonreaders. A copy of his thesis, "Approximation Algorithms on Network Routing and Facility Location Problems" can be viewed by contacting his advisor. Everyone is invited to attend his talk.

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H Nguyen preFPO
by Melissa M. Lawson 27 Nov '13

27 Nov '13

Huy Nguyen will present his preFPO on Friday December 6 at 4:30 PM in Room 402. The members of his committee are: Moses Charikar, advisor; Bernard Chazelle and Piotr Indyk (MIT), readers; Sanjeev Arora and Mark Braverman, nonreaders. Everyone is invited to attend his talk. His abstract follows below. ----- Original Message ----- Title: Algorithms for Data in High Dimensions In the last decade, the availability of large data sets in high dimensions has been both an opportunity and a challenge for algorithm design. Many new algorithms are invented and yet many challenges remain unconquered. This thesis examines several methods for handling such data efficiently in terms of time and space, their limitations, and new techniques to get beyond those limitations. First, we look at embeddings, a powerful technique to reduce data in high dimensions to low dimensions. Tight or near tight bounds are shown for sparse subspace embeddings, sparse Johnson-Lindenstrauss transforms, and sparse RIP matrices. We also touch upon applications of subspace embeddings in numerical linear algebra and approximating large eigenvalues in both the classical setting and the streaming setting. For instance, the algorithm implied by our bound for subspace embeddings runs in time linear in the sparsity of the input plus an additive term that is optimal unless one can bypass generic matrix multiplication. To conclude the embedding section, we show a near linear lower bound for dimension reduction in L1. This bound is close to optimal as one can reduce the dimensions to linear while approximately preserving all pairwise distances. Even though significant dimension reduction is not possible in L1, fortunately there is a technique called locality sensitive hashing (LSH) allowing for efficient approximate nearest neighbor search in L1. In the second section of the thesis, we give a new construction of LSH for L_p (1<p<2) that is close to the lower bound for LSH. The algorithm is a generalization of previous work for L2 via the uniform convexity property. Lastly, in the final section of the thesis, we overcome the aforementioned lower bound for LSH and design a new algorithm for approximate nearest neighbor search in L1 and L2 that is faster and uses less memory than any LSH. Unlike LSH, the algorithm exploits the structure of the whole point set, and can be viewed as a step toward understanding data-aware methods for nearest neighbor search, which are ubiquitous in practice.

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