2015 Informs Annual Meeting

SB20

INFORMS Philadelphia – 2015

SB20 20-Franklin 10, Marriott Cloud Resource Management and Pricing Cluster: Cloud Computing Invited Session Chair: Daniel Grosu, Wayne State University, 5057 Woodward Ave, Detroit, United States of America, dgrosu@wayne.edu Co-Chair: Lena Mashayekhy, mlena@wayne.edu 1 - Efficient and Online Reconfigurations in NoSQL Databases Indranil Gupta, Associate Professor, University of Illinois at Urbana-Champaign, 201 N. Goodwin Ave., Urbana, IL, 61801, United States of America, indy@illinois.edu, Mainak Ghosh, Yosub Shin, Wenting Wang, Gopalakrishna Holla NoSQL databases are soon to be a multi-billion dollar market. In live NoSQL deployments “reconfiguration operations,” affecting a lot of data at once, are a major pain point, e.g., changing a table’s primary key. We describe techniques to perform reconfiguration in NoSQL systems (sharded and ring-based) quickly and online, i.e., while supporting high availability and low latency for reads/writes. We present results from our implementation and deployment with popular open- source NoSQL systems. 2 - Exploring Market Models for Software-defined Systems Manish Parashar, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ, 08854, United States of America, parashar@rutgers.edu Software-defined platforms, such as those enabled by Cloud services, provide new levels of flexibility, which can lead to very dynamic infrastructures that adapt themselves to application needs. In this talk I will explore how a cloud-of-clouds marketplace can support data-driven applications by programmatically federating geographically distributed resources to satisfy QoS demands. Specifically, I will focus on market and utility based models that can drive the synthesis of such systems. 3 - Stochastic Optimal Control of Time-varying Workloads Mark Squillante, IBM Research, Thomas J. Watson Research

3 - Outcome-driven Personalized Treatment Design for Managing Diabetes

Eva Lee, Georgia Institute of Technology, eva.lee@gatech.edu This work is joint with Grady Memorial Hospital and the Atlanta VA Medical Center. We discuss an evidence-based decision support tool that couples a treatment predictive model with a planning model. Specifically, the predictive model uncovers drug effect based on pharmaco-kinetics and dynamics analysis. This evidence is then modeled within the personalized planning model for optimal treatment plan design. Results for a collection of patients will be presented. SB22 22-Franklin 12, Marriott Experiment Design and A-B Testing Sponsor: Applied Probability Sponsored Session Chair: Ciamac Moallemi, Columbia Business School, 3022 Broadway, Uris Hall, New York, United States of America, ciamac@gsb.columbia.edu Co-Chair: Vivek Farias, Associate Professor, MIT, 100 Main Street, Cambridge, MA, United States of America, vivekf@mit.edu 1 - Online A-B Testing Vivek Farias, Associate Professor, MIT, 100 Main Street, Cambridge, MA, United States of America, vivekf@mit.edu, Nikhil Bhat, Ciamac Moallemi We consider the problem of optimal A-B testing when the impact of a treatment is marred by a large number of covariates and subjects arrive and are assigned to a treatment sequentially. Our objective is to maximize the efficiency of our estimate of the treatment effect. Our main contribution is to show that what is typically thought of as a high-dimensional, intractable problem is, in fact, tractable under reasonable assumptions. 2 - Covariate Balanced Restricted Randomization: Optimal Designs, Exact Tests, and Asymptotic Results José Zubizarreta, Columbia Business School, 3022 Broadway, Uris Hall, Room 417, New York, NY, 10027, United States of America, jz2313@columbia.edu, Jingjing Zou We present a new method for the design of randomized experiments that (i) uses integer programming to optimally match subjects before randomization, (ii) assigns matched subjects to treatment and control in a randomized but controlled or restricted fashion, (iii) yields an explicit exact randomization distribution for small samples, and (iv) has good asymptotic properties in large experiments. We illustrate this method with a real experiment and show its advantages beyond standard methods. 3 - Can I Take a Peek? Continuous Monitoring of A/B Tests Ramesh Johari, Stanford University, 475 Via Ortega, Stanford, Ca, 94305, United States of America, ramesh.johari@stanford.edu, Leo Pekelis, David Walsh Statistical results for A/B tests are computed under the assumption that the experimenter will not continuously monitor their test. If users continuously monitor experiments, as is common in practice, then what statistical methodology is appropriate for hypothesis testing, significance, and confidence intervals? We present recent work addressing this question, building from results in sequential hypothesis testing. This work was carried out with Optimizely, a leading A/B testing platform.

Center, 1101 Kitchawan Road, Yorktown Heights, NY, United States of America, mss@us.ibm.com, Yingdong Lu, Mayank Sharma, Bo Zhang

Motivated by cloud computing, we consider GI/GI/1 queueing systems under time-varying workloads on one time scale and under time-varying controls on another time scale. We derive structural properties for the optimal dynamic control policy in general, establishing that this policy can be obtained through a sequence of convex programs. We also derive fluid and diffusion approximations and solutions for the problem. Computational experiments demonstrate the benefits of our approach.

SB21 21-Franklin 11, Marriott Healthcare Data Analytics Sponsor: Health Applications Sponsored Session

Chair: Donald Lee, Yale School of Management, 165 Whitney Ave, New Haven, CT, United States of America, donald.lee@yale.edu 1 - A Statistical Approach to Cost-effectiveness Analysis under Uncertainty about the Willingness-to-pay Reza Yaesoubi, Yale School of Public Health, 60 College Street, New Haven, CT, 06510, United States of America, reza.yaesoubi@yale.edu, Forrest Crawford, David Paltiel Although it plays a central role in cost-effectiveness analysis, societies’ willingness to invest for an additional unit of health is rarely known to policy makers. In this work, we develop a statistical model to help decision makers determine whether a new healthcare alternative is considered cost-effective in the absence of exact value for the willingness-to-pay for health. 2 - Networks Classification via Mathematical Programming Daehan Won, University of Washington, Seattle, 1415 NE Ravenna Blvd, #401, Seattle, WA, 98105, United States of America, wondae@uw.edu We are developing mathematical programming models to classify the network structured data. Along the line with the feature selection, we present node selection approach to increase the classification accuracy as well as improving interpretability. To verify the utility of our proposed approach, we demonstrate the result of brain functional connectivity network data set.

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