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Lin Yuan

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

    Чт, 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.

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

    Пт, 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.

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

    Ср, 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.

  4. Automatic Performance Debugging of SPMD Parallel Programs.

    Ср, 02/24/2010 - 16:01 — SomNambul
    Authors: Xu Liu, Lin Yuan, Jianfeng Zhan, Bibo Tu, Dan Meng
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    Automatic performance debugging of parallel applications usually involves two
    steps: automatic detection of performance bottlenecks and uncovering their root
    causes for performance optimization. Previous work fails to resolve this
    challenging issue in several ways: first, several previous efforts automate
    analysis processes, but present the results in a confined way that only
    identifies performance problems with apriori knowledge; second, several tools
    take exploratory or confirmatory data analysis to automatically discover
    relevant performance data relationships.

Math-arch

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