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Lei Wang

  1. Learning Valuation Functions.

    Tue, 08/30/2011 - 15:01 — SomNambul
    Authors: Lei Wang, Florin Constantin, Maria Florina Balcan, Satoru Iwata
    Subjects: Computer Science and Game Theory
    Abstract

    In this paper we study the approximate learnability of valuations commonly
    used throughout economics and game theory for the quantitative encoding of
    agent preferences. We provide upper and lower bounds regarding the learnability
    of important subclasses of valuation functions that express
    no-complementarities. Our main results concern their approximate learnability
    in the distributional learning (PAC-style) setting.

  2. The first returning speed and the last exit speed of a type of Markov chain.

    Fri, 01/07/2011 - 16:01 — SomNambul
    Authors: Lei Wang, Huizeng Zhang, Minzhi zhao
    Subjects: Probability
    Abstract

    Let $\{X_n\}$ be a Markov chain with transition probability
    $p_{ij}=a_{j-(i-1)^+},\forall i,j\ge 0$, where $a_j=0$ provided $j<0$, $a_0>0$,
    $a_0+a_1<1$ and $\sum_{n=0}^\infty a_n=1$. Let $\mu=\sum_{n=1}^\infty na_n$.
    It's known that $\{X_n\}$ is positive recurrent when $\mu<1$; is null recurrent
    when $\mu=1$; and is transient when $\mu>1$. In this paper, we shall discuss
    the first returning speed and the last exit speed more precisely by means of
    $\{a_n\}$

  3. PowerTracer: Tracing requests in multi-tier services to save cluster power consumption.

    Thu, 07/29/2010 - 15:01 — SomNambul
    Authors: Lei Wang, Lin Yuan, Jianfeng Zhan, Bo Sang, Haining Wang
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    As energy proportional computing has extended the success of DVFS (Dynamic
    voltage and frequency scaling) to the entire system, DVFS control algorithms
    will play a key role in reducing server clusters' power consumption. The focus
    of this paper is to provide accurate cluster-level DVFS control for power
    saving in a server cluster. To achieve this goal, we propose a request tracing
    approach that online classifies the major causal path patterns and monitors
    their performance data as a guide for accurate DVFS control.

  4. Single Parameter Combinatorial Auctions with Partially Public Valuations.

    Thu, 07/22/2010 - 15:01 — SomNambul
    Authors: Gagan Goel, Lei Wang, Chinmay Karande
    Subjects: Computer Science and Game Theory
    Abstract

    We consider the problem of designing truthful auctions, when the bidders'
    valuations have a public and a private component. In particular, we consider
    combinatorial auctions where the valuation of an agent $i$ for a set $S$ of
    items can be expressed as $v_if(S)$, where $v_i$ is a private single parameter
    of the agent, and the function $f$ is publicly known. Our motivation behind
    studying this problem is two-fold: (a) Such valuation functions arise naturally
    in the case of ad-slots in broadcast media such as Television and Radio.

  5. In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale?.

    Fri, 04/09/2010 - 15:02 — SomNambul
    Authors: Lei Wang, Lin Yuan, Jianfeng Zhan, Weisong Shi, Yi Liang
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    In this paper, we intend to answer one key question to the success of cloud
    computing: in cloud, do many task computing (MTC) or high throughput computing
    (HTC) service providers, which offer the corresponding computing service to end
    users, benefit from the economies of scale? Our research contributions are
    three-fold: first, we propose an innovative usage model, called dynamic service
    provision (DSP) model, for MTC or HTC service providers.

  6. In Cloud, Can Scientific Communities Benefit from the Economies of Scale?.

    Fri, 04/09/2010 - 15:01 — SomNambul
    Authors: Lei Wang, Jianfeng Zhan, Weisong Shi, Yi Liang
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    The basic idea behind Cloud computing is that resource providers offer
    elastic resources to end users. In this paper, we intend to answer one key
    question to the success of Cloud computing: in Cloud, can small or medium-scale
    scientific computing communities benefit from the economies of scale?

  7. Scalable Group Management in Large-Scale Virtualized Clusters.

    Wed, 03/31/2010 - 15:02 — SomNambul
    Authors: Lei Wang, Lin Yuan, Jianfeng Zhan, Dan Meng, Wei Zhou
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    To save cost, recently more and more users choose to provision virtual
    machine resources in cluster systems, especially in data centres. Maintaining a
    consistent member view is the foundation of reliable cluster managements, and
    it also raises several challenge issues for large scale cluster systems
    deployed with virtual machines (which we call virtualized clusters). In this
    paper, we introduce our experiences in design and implementation of scalable
    member view management on large-scale virtual clusters.

  8. PhoenixCloud: Provisioning Runtime Environments for Heterogeneous Cloud Workloads.

    Fri, 03/05/2010 - 16:01 — SomNambul
    Authors: Lei Wang, Jianfeng Zhan, Weisong Shi, Shimin Gong, Xiutao Zang
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    As more and more service providers choose Cloud platforms, a resource
    provider needs to provision runtime environments (REs) for heterogeneous
    workloads in different scenarios. Previous work fails to resolve this issue in
    several ways: (1) it fails to pay attention to diverse RE requirements, and
    does not enable creating coordinated REs on demand; (2) few work investigates
    coordinated resource provisioning for heterogeneous workloads.

  9. Precise Request Tracing and Performance Debugging for Multi-tier Services of Black Boxes.

    Fri, 03/05/2010 - 16:01 — SomNambul
    Authors: Lei Wang, Jianfeng Zhan, Dan Meng, Yong Li, Zhihong Zhang, Bo Sang
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    As more and more multi-tier services are developed from commercial components
    or heterogeneous middleware without the source code available, both developers
    and administrators need a precise request tracing tool to help understand and
    debug performance problems of large concurrent services of black boxes.
    Previous work fails to resolve this issue in several ways: they either accept
    the imprecision of probabilistic correlation methods, or rely on knowledge of
    protocols to isolate requests in pursuit of tracing accuracy.

  10. Scalable Large-Margin Mahalanobis Distance Metric Learning.

    Wed, 03/03/2010 - 16:01 — SomNambul
    Authors: Chunhua Shen, Junae Kim, Lei Wang
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN)
    classifiers and $ k $-means clustering, often their success heavily depends on
    the metric used to calculate distances between different data points.

  11. Phoenix Cloud : Consolidating Heterogeneous Workloads of Large Organizations on Cloud Computing Platforms.

    Wed, 02/24/2010 - 16:02 — SomNambul
    Authors: Lei Wang, Jianfeng Zhan, Bibo Tu, Dan Meng, Yong Li, Peng Wang, Wei Zhou
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    For a large organization, different departments often maintain dedicated
    cluster systems for different workloads, for example parallel batch jobs or Web
    services. In this paper, we design and implement an innovative cloud computing
    system software, Phoenix Cloud, to consolidate heterogeneous workloads of the
    same organization on cloud computing platforms. For Phoenix Cloud, we propose
    cooperative resource provision and management polices for the affiliated
    departments of a large organization to share cluster systems.

  12. Optimal Approximation Algorithms for Multi-agent Combinatorial Problems with Discounted Price Functions.

    Mon, 11/09/2009 - 16:01 — SomNambul
    Authors: Gagan Goel, Lei Wang, Pushkar Tripathi
    Subjects: Multiagent Systems
    Abstract

    Submodular functions are an important class of functions in combinatorial
    optimization which satisfy the natural properties of decreasing marginal costs.
    The study of these functions has led to strong structural properties with
    applications in many areas.

  13. Positive Semidefinite Metric Learning with Boosting.

    Wed, 10/14/2009 - 11:03 — SomNambul
    Authors: Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    The learning of appropriate distance metrics is a critical problem in image
    classification and retrieval. In this work, we propose a boosting-based
    technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One
    of the primary difficulties in learning such a metric is to ensure that the
    Mahalanobis matrix remains positive semidefinite.

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