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Analytic Model

The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Evgenia Smirni – 1st expert on this subject based on the ideXlab platform

  • A regression-based Analytic Model for capacity planning of multi-tier applications
    Cluster Computing, 2008
    Co-Authors: Qi Zhang, Ludmila Cherkasova, Ningfang Mi, Evgenia Smirni

    Abstract:

    The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective Models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making Model adaptivity to the observed workload changes a critical requirement for Model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then, we use this approximation in an Analytic Model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for Modeling diverse workloads with a changing transaction mix over time. Using two case studies, we investigate factors that impact the efficiency and accuracy of the proposed performance prediction Models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

  • A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
    Fourth International Conference on Autonomic Computing (ICAC'07), 2007
    Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia Smirni

    Abstract:

    The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective Models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making Model adaptivity to the observed workload changes a critical requirement for Model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an Analytic Model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for Modeling diverse workloads with a changing transaction mix over time. Using the TPC- W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction Models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

  • ICAC – A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
    Fourth International Conference on Autonomic Computing (ICAC'07), 2007
    Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia Smirni

    Abstract:

    The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective Models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making Model adaptivity to the observed workload changes a critical requirement for Model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an Analytic Model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for Modeling diverse workloads with a changing transaction mix over time. Using the TPC- W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction Models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

Qi Zhang – 2nd expert on this subject based on the ideXlab platform

  • A regression-based Analytic Model for capacity planning of multi-tier applications
    Cluster Computing, 2008
    Co-Authors: Qi Zhang, Ludmila Cherkasova, Ningfang Mi, Evgenia Smirni

    Abstract:

    The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective Models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making Model adaptivity to the observed workload changes a critical requirement for Model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then, we use this approximation in an Analytic Model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for Modeling diverse workloads with a changing transaction mix over time. Using two case studies, we investigate factors that impact the efficiency and accuracy of the proposed performance prediction Models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

  • A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
    Fourth International Conference on Autonomic Computing (ICAC'07), 2007
    Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia Smirni

    Abstract:

    The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective Models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making Model adaptivity to the observed workload changes a critical requirement for Model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an Analytic Model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for Modeling diverse workloads with a changing transaction mix over time. Using the TPC- W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction Models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

  • ICAC – A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
    Fourth International Conference on Autonomic Computing (ICAC'07), 2007
    Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia Smirni

    Abstract:

    The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective Models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making Model adaptivity to the observed workload changes a critical requirement for Model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an Analytic Model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for Modeling diverse workloads with a changing transaction mix over time. Using the TPC- W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction Models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

Eloisa Izco – 3rd expert on this subject based on the ideXlab platform

  • power output fluctuations in large scale pv plants one year observations with one second resolution and a derived Analytic Model
    Progress in Photovoltaics, 2011
    Co-Authors: Javier Marcos, Luis Marroyo, Eduardo Lorenzo, David Alvira, Eloisa Izco

    Abstract:

    The variable nature of the irradiance can produce significant fluctuations in the power generated by large grid-connected photovoltaic (PV) plants. Experimental 1 s data were collected throughout a year from six PV plants, 18 MWp in total. Then, the dependence of short (below 10 min) power fluctuation on PV plant size has been investigated. The analysis focuses on the study of fluctuation frequency as well as the maximum fluctuation value registered. An Analytic Model able to describe the frequency of a given fluctuation for a certain day is proposed

  • Power output fluctuations in large scale pv plants: One year observations with one second resolution and a derived Analytic Model
    Progress in Photovoltaics: Research and Applications, 2011
    Co-Authors: Javier Marcos, Luis Marroyo, Eduardo Lorenzo, David Alvira, Eloisa Izco

    Abstract:

    The variable nature of the irradiance can produce significant fluctuations in the power generated by large grid-connected photovoltaic (PV) plants. Experimental 1 s data were collected throughout a year from six PV plants, 18 MWp in total. Then, the dependence of short (below 10 min) power fluctuation on PV plant size has been investigated. The analysis focuses on the study of fluctuation frequency as well as the maximum fluctuation value registered. An Analytic Model able to describe the frequency of a given fluctuation for a certain day is proposed. © 2010 John Wiley & Sons, Ltd.