Research

Stochastic systems with applications in data centers and cloud computing

I am interested in characterizing fundamental limits of large-scale computing systems that address emerging demands from big-data analytics, and designing algorithms with optimality on throughput and latency performance. My research in this area focuses on the following aspects.

  • Latency characterization. Latency is a crucial performance metric in technologies nowadays, especially in interactive applications such as real-time machine learning, online transactions, and video streaming. Amazon has calculated that a page load slowdown of just one second can cost $1.6 billion in sales each year. My research in this area provides tight characterizations of latency in large-scale computing systems, enabling identification of performance bottlenecks and informing optimal designs of scheduling and resource allocation algorithms.

  • Coordination of data and computation. Big-data analytics has led to a paradigm shift from computation-centric computing systems to systems where data plays an equally, if not more, important role. Data and computation are closely correlated in present computing systems. For example, data-parallel frameworks such as MapReduce/Hadoop and Spark associate a computing task with a chunk of data, where the computation can be executed only when both the associated data and a computation slot on servers are available. My research takes the lead in addressing the coordination of data and computation, and designs scheduling algorithms with rigorous theoretical guarantees on performance optimality.

  • Large-scale regimes. To accommodate the growing computational demand, modern data centers are scaled up in size, consisting of tens of thousands of servers. Such large-scale systems have attracted great attention both from practice and from theoretical research. My research investigates the scaling behavior of performance with respect to the size of system infrastructures, including the number of servers, the number of communication links, etc. My research further explores new large-scale regimes to capture the large volume of data. In particular, I study jobs whose sizes expand as the system scales, which models the trend that computational jobs are processing larger and larger volumes of data.

Data privacy

I work on data privacy and its intersection with other areas including game theory, information theory and statistics. Our recent focus is a new, market model that we envisage for collecting private data where data subjects (individuals) retain full control of their privacy. I aim to understand the economic fundamentals of collecting private data and design optimal incentive mechanisms.

Publications

Stochastic systems

  • Delay asymptotics and bounds for multi-task parallel jobs
    Weina Wang, Mor Harchol-Balter, Haotian Jiang, Alan Scheller-Wolf, and R. Srikant
    Queueing Systems Jan. 2019. [DOI]
    International Symposium on Computer Performance, Modeling, Measurements and Evaluation (IFIP Performance), Toulouse, France, Dec. 2018. [DOI]
    [QUESTA pdf]

  • Heavy-traffic insensitive bounds for weighted proportionally fair bandwidth sharing policies
    Weina Wang, Siva Theja Maguluri, R. Srikant, and Lei Ying
    ArXiv, 2018. [link]
    Heavy-traffic delay insensitivity in connection-level models of data transfer with proportionally fair bandwidth sharing
    Weina Wang, Siva Theja Maguluri, R. Srikant, and Lei Ying
    International Symposium on Computer Performance, Modeling, Measurements and Evaluation (IFIP Performance), New York City, NY, Dec. 2017. [DOI]

  • Resource allocation for data-parallel computing in networks with data locality
    Weina Wang and Lei Ying
    Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2016. [DOI]
    [Allerton pdf]

  • Decentralized scheduling with data locality for data-parallel computation on Peer-to-Peer networks
    Weina Wang, Matthew Barnard and Lei Ying
    Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2015. [DOI]
    [Allerton pdf]

  • Data locality in MapReduce: A network perspective
    Weina Wang and Lei Ying
    Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2014. [DOI]
    Performance Evaluation, Feb. 2016. [DOI]
    [PEVA pdf] [technical report]

  • MapTask scheduling in MapReduce with data locality: throughput and heavy-traffic optimality Weina Wang, Kai Zhu, Lei Ying, Jian Tan, and Li Zhang
    IEEE/ACM Transactions on Networking, Feb. 2016. [DOI]
    Map task scheduling in MapReduce with data locality: throughput and heavy-traffic optimality Weina Wang, Kai Zhu, Lei Ying, Jian Tan, and Li Zhang
    IEEE International Conference on Computer Communications (INFOCOM), Turin, Italy, Apr. 2013. [DOI]
    A throughput optimal algorithm for map task scheduling in mapreduce with data locality
    Weina Wang, Kai Zhu, Lei Ying, Jian Tan, and Li Zhang
    ACM SIGMETRICS Performance Evaluation Review, Mar. 2013. [DOI]
    [ToN pdf] [INFOCOM pdf] [PER version] [poster]

  • On the performance of largest-deficit-first for scheduling real-time traffic in wireless networks
    Xiaohan Kang, Weina Wang, Juan José Jaramillo, and Lei Ying
    IEEE/ACM Transactions on Networking, Feb. 2016. [DOI]
    ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Bangalore, India, Jul. 2013. [DOI]
    [ToN pdf] [supplementary] [MobiHoc pdf]

Data privacy

  • A winners-take-all incentive mechanism for crowd-powered systems
    Pengfei Jiang, Weina Wang, Yao Zhu, Jingrui He, and Lei Ying
    Workshop on Economics of Networks, Systems and Computation (NetEcon), Jun. 2018. [DOI]

  • Buying data from privacy-aware individuals: The effect of negative payments
    Weina Wang, Lei Ying, and Junshan Zhang
    Conference on Web and Internet Economics (WINE), Montreal, Canada, Dec. 2016. [DOI]
    [WINE pdf] [technical report]

  • The value of privacy: Strategic data subjects, incentive mechanisms and fundamental limits
    Weina Wang, Lei Ying, and Junshan Zhang
    ACM Transactions on Economics and Computation, Aug. 2018. [DOI]
    ACM SIGMETRICS, Antibes Juan-les-Pins, France, Jun. 2016. [DOI]
    Kenneth C. Sevcik Outstanding Student Paper Award
    [TEAC pdf] [SIGMETRICS pdf]

  • A game-theoretic approach to quality control for collecting privacy-preserving data
    Weina Wang, Lei Ying, and Junshan Zhang
    Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2015. [DOI]
    [Allerton pdf] [technical report]

  • A minimax distortion view of differentially private query release Weina Wang, Lei Ying, and Junshan Zhang
    Asilomar Conference on Signals, Systems, and Computers (Asilomar), Pacific Grove, CA, Nov. 2015. [DOI] [Asilomar pdf] [technical report]

  • On the relation between identifiability, differential privacy, and mutual-information privacy
    Weina Wang, Lei Ying, and Junshan Zhang
    IEEE Transactions on Information Theory, Sep. 2016. [DOI]
    Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2014. [DOI]
    Poster presented at IEEE North American School of Information Theory, Toronto, Canada, Jun. 2014. [TIT pdf] [poster]

Machine learning

  • Almost Boltzmann exploration
    Harsh Gupta, Seo Taek Kong, Weina Wang, and R. Srikant
    ArXiv, Jan. 2019. [link]

People

Faculty

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Weina Wang

Assistant Professor

PhD Students

Jalani Williams

PhD Student

Undergraduate Students

Wenxin Ding

Undergraduate Student

Administration

Rosie Battenfelder

Administration

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