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, 2008Co-Authors: Qi Zhang, Ludmila Cherkasova, Ningfang Mi, Evgenia SmirniAbstract: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), 2007Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia SmirniAbstract: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), 2007Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia SmirniAbstract: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, 2008Co-Authors: Qi Zhang, Ludmila Cherkasova, Ningfang Mi, Evgenia SmirniAbstract: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), 2007Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia SmirniAbstract: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), 2007Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia SmirniAbstract: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.
-
power output fluctuations in large scale pv plants one year observations with one second resolution and a derived Analytic Model
Progress in Photovoltaics, 2011Co-Authors: Javier Marcos, David Alvira, Eduardo Lorenzo, Luis Marroyo, Eloisa IzcoAbstract: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, 2011Co-Authors: Javier Marcos, David Alvira, Eduardo Lorenzo, Luis Marroyo, Eloisa IzcoAbstract: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.
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, 2014Co-Authors: Derek DoranAbstract: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, 2013Co-Authors: Derek Doran, Swapna Gokhale, Nicholas LownesAbstract: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, 2008Co-Authors: Qi Zhang, Ludmila Cherkasova, Ningfang Mi, Evgenia SmirniAbstract: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), 2007Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia SmirniAbstract: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), 2007Co-Authors: Qi Zhang, Ludmila Cherkasova, Evgenia SmirniAbstract: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.