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Conferences, Lectures, & Seminars
Events for May
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Mor Harchol-Balter: Dynamic Power Management in Data Centers: Theory & Practice
Mon, May 06, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mor Harchol-Balter, Carnegie Mellon University
Talk Title: Dynamic Power Management in Data Centers: Theory & Practice
Series: CS Colloquium
Abstract: Energy costs for data centers continue to rise, already exceeding ten billion dollars yearly. Sadly much of this power is wasted. Server are only busy 10-30% of the time, but they are often left on, while idle, utilizing 60% of more of peak power while in the idle state. The obvious solution is dynamic power management: turning servers off, or re-purposing them, when idle. The drawback is a prohibitive "setup cost" to get servers back "on." The purpose of this talk is to understand the effect of the "setup cost" and whether dynamic power management makes sense.
We first turn to theory and study the effect of setup cost in an M/M/k queue. We present the first analysis of the M/M/k/setup queueing system. We do this by introducing a new technique for analyzing infinite, repeating, continuous-time Markov chains, which we call Recursive Renewal Reward (RRR).
We then turn to implementation, where we implement and evaluate
dynamic power management in a multi-tier data center with key-value store workload, reminiscent of Facebook or Amazon. We propose a new dynamic algorithm, AutoScale, which is ideally suited to the case of unpredictable, time-varying load, and we show that AutoScale dramatically reduces power in data centers.
Joint work with: Anshul Gandhi, Alan Scheller-Wolf, and Mike Kozuch.
Biography: Mor Harchol-Balter is an Associate Professor in Computer Science at Carnegie Mellon University. From 2008-2011, she served as the
Associate Department Head for Computer Science. She received her
doctorate in Computer Science at U.C. Berkeley under the direction of Manuel Blum. She is a recipient of the McCandless Chair, the NSF CAREER award, the NSF Postdoctoral Fellowship in the Mathematical Sciences, multiple best paper awards, and several teaching awards, including the Herbert A. Simon Award for Teaching Excellence. She is heavily involved in the ACM SIGMETRICS performance research community, where she served as Technical Program Chair for Sigmetrics 2007 and is General Chair for Sigmetrics 2013.
Host: Leana Golubchik
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Distinguished Lecture: Eric Xing (CMU) - Big Data, Big Model, and Big Learning
Tue, May 21, 2013 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Eric Xing, Carnegie Mellon University
Talk Title: Big Data, Big Model, and Big Learning
Series: CS Distinguished Lectures
Abstract: In many modern applications built on massive data, such as societal-scale event detection, social security and privacy, web commerce and marketing, and personalized medicine, one needs to handle extremely large-scale data and models that threaten to exceed the limit of current infrastructures and algorithms. Due to the extremely large volume, high dimensionality, and massive task complexity associated with this applications, many modern advancements in computational and statistical learning have been rendered un-leverageable due to their poor scalability on ultra-dimensional models and inability to extract values from massive data; practitioners are forced to turn to naive alternatives such as KNN or K-means cluster for complex problems purely due to their simplicity and scalability, but not for their model validity and correctness. In this talk, I will present some thoughts and work on big learning problems in web-scale social data mining, computational biology, and computer vision. I will discuss some insights and promising directions toward large data size, large feature dimension, and large concept space, including parallelizable and online Monte Carlo for infinite dynamic topic models, fast 1st-order convex optimization algorithms for learning ultra high-dimensional sparse structured input/output models, and output coding techniques for massive multi-task and transfer learning, and I will discuss the design and issues of low level computer architecture and operating systems supporting large learning, applied to a wide range of problems.
Biography: Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in social and biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) large-scale information & intelligent system in social networks, computer vision, and natural language processing. Professor Xing has published over 180 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, the IBM Open Collaborative Research Award, and best paper awards in a number of premier conferences including UAI, ACL, SDM, and ISMB.
Host: Gaurav Sukhatme, Michael Waterman
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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An Introduction to Rethink Robotics' New Baxter Research Robot (BRR)
Thu, May 30, 2013 @ 01:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: ,
Talk Title: An Introduction to Rethink Robotics' New Baxter Research Robot (BRR), Including Product Overview Presentation and Baxter Manufacturing Robot(BMR) Live Demo
Abstract: The Baxter Research Robot leverages the same impressive safety features, ease of use and affordability as the original Baxter robot for manufacturing, while offering several added characteristics that make it an ideal fit for labs. The Baxter Research Robot allows research teams to focus on specific application development goals, including human-robot interaction, collaborative robotics, planning, manipulation, control, and perception. Whether your focus is product testing or developing the next big innovation in robotics, Baxter Research Robot is the ideal platform for your success. Our 30min presentation on the new Baxter Research Robot (BRR) will be followed by a live demo of the Baxter robot for manufacturing, the "sister product" to the BRR and will last approximately 1 to 1.5 hours total.
Host: Stefan Schaal
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
