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

Evgenia Smirni - One of the best experts 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 - One of the best experts 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 - One of the best experts on this subject based on the ideXlab platform.

Derek Doran - One of the best experts on this subject based on the ideXlab platform.

  • An Analytic Model of Airport Security Checkpoint Screening Times
    Transportation Research Board 93rd Annual Meeting, 2014
    Co-Authors: Derek Doran
    Abstract:

    Security checkpoints at airports across the United States are essential to prevent passengers from boarding airplanes with dangerous weapons, explosives, and other threats. However, the multiple screening technologies and different speeds of passengers lead to unpredictable, and sometimes long waiting times. Security agencies and airport managers must thus find ways to minimize checkpoint screening times without compromising the security of aviation transportation. This paper introduces an Analytic Model that derives the distribution of completion times for passengers through a security checkpoint given its architecture, passenger profiles, and expected service times at different checkpoint components. By varying the Model's parameters and checkpoint architecture, security agencies and airport managers can quickly understand how the end-to-end completion times of passengers are affected by policy changes and checkpoint reconfigurations. The Model can also be used to forecast the performance of future checkpoint architectures utilizing new components and polices. We demonstrate the utility of the Model by analyzing a prototypical security checkpoint.

  • Analytic Model of Screening Times at Airport Security Checkpoints
    Transportation Research Record: Journal of the Transportation Research Board, 2013
    Co-Authors: Derek Doran, Swapna Gokhale, Nicholas Lownes
    Abstract:

    Security checkpoints at airports across the United States are essential for preventing passengers with dangerous weapons, explosives, and other threats from boarding airplanes. However, the multiple screening technologies and speeds of passengers lead to unpredictable and sometimes long waiting times. Security agencies and airport managers must find ways to minimize screening times at checkpoints without compromising the security of aviation transportation. This paper introduces an Analytic Model that derives the distribution of completion times for passengers through a security checkpoint, given its architecture, passenger profiles, and expected service times at checkpoint components. By varying the Model's parameters and checkpoint architecture, security agencies and airport managers can quickly understand how the end-to-end completion times of passengers are affected by policy changes and checkpoint reconfigurations. The Model can also be used to forecast the performance of future checkpoint architectures that use new components and policies. The authors demonstrate the utility of the Model by analyzing a prototypical security checkpoint.

Ludmila Cherkasova - One of the best experts 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.