The Experts below are selected from a list of 125493 Experts worldwide ranked by ideXlab platform
Harry Podschwit - One of the best experts on this subject based on the ideXlab platform.
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high severity wildfire potential associating meteorology climate Resource Demand and wildfire activity with preparedness levels
International Journal of Wildland Fire, 2021Co-Authors: Alison C. Cullen, Travis Axe, Harry PodschwitAbstract:National and regional preparedness level (PL) designations support decisions about wildfire risk management. Such decisions occur across the fire season and influence pre-positioning of Resources in areas of greatest fire potential, recall of personnel from off-duty status, requests for back-up Resources from other areas, responses to requests to share Resources with other regions during fire events, and decisions about fuel treatment and risk reduction, such as prescribed burning. In this paper, we assess the association between PLs assigned at national and regional (Northwest) scales and a set of predictors including meteorological and climate variables, wildfire activity and the mobilisation and allocation levels of fire suppression Resources. To better understand the implicit weighting applied to these factors in setting PLs, we discern the qualitative and quantitative factors associated with PL designations by statistical analysis of the historical record of PLs across a range of conditions. Our analysis constitutes an important step towards efforts to forecast PLs and to support the future projection and anticipation of firefighting Resource Demand, thereby aiding wildfire risk management, planning and preparedness.
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High-severity wildfire potential – associating meteorology, climate, Resource Demand and wildfire activity with preparedness levels
International Journal of Wildland Fire, 2021Co-Authors: Alison C. Cullen, Travis Axe, Harry PodschwitAbstract:National and regional preparedness level (PL) designations support decisions about wildfire risk management. Such decisions occur across the fire season and influence pre-positioning of Resources in areas of greatest fire potential, recall of personnel from off-duty status, requests for back-up Resources from other areas, responses to requests to share Resources with other regions during fire events, and decisions about fuel treatment and risk reduction, such as prescribed burning. In this paper, we assess the association between PLs assigned at national and regional (Northwest) scales and a set of predictors including meteorological and climate variables, wildfire activity and the mobilisation and allocation levels of fire suppression Resources. To better understand the implicit weighting applied to these factors in setting PLs, we discern the qualitative and quantitative factors associated with PL designations by statistical analysis of the historical record of PLs across a range of conditions. Our analysis constitutes an important step towards efforts to forecast PLs and to support the future projection and anticipation of firefighting Resource Demand, thereby aiding wildfire risk management, planning and preparedness.
Edmundo R. M. Madeira - One of the best experts on this subject based on the ideXlab platform.
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NOMS - Reliable Network Slices based on Elastic Network Resource Demand
NOMS 2020 - 2020 IEEE IFIP Network Operations and Management Symposium, 2020Co-Authors: Gabriel V.l. Da Silva, Dyego H. L. Oliveira, Rafael L. Gomes, Luiz F. Bittencourt, Edmundo R. M. MadeiraAbstract:Internet access is crucial to the human society as a platform for several services to the users. Despite this importance, the Internet suffers limitations that compromise Quality of Service (QoS) guarantees. Thus, Internet Service Providers (ISPs) need to evolve, adding new technologies and management strategies to their infrastructure. A promising approach is the slicing of network Resources among clients and delivered services, where reliability and elastic Resource Demand through the day are key issues. Within this context, this paper presents an algorithm called Reliable Reuse Encourage (R-REENC), which defines network slices based on bandwidth requirements and the desired reliability for the clients. The results suggest that the proposed algorithm allocates more suitable slices than other existing approaches.
Samuel Kounev - One of the best experts on this subject based on the ideXlab platform.
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Resource Demand Estimation
Systems Benchmarking, 2020Co-Authors: Samuel Kounev, Klaus-dieter Lange, Jóakim Von KistowskiAbstract:In this chapter, we survey, systematize, and evaluate different approaches to the statistical estimation of Resource Demands based on easy-to-measure system-level and application-level metrics. We consider Resource Demands in the context of computing systems; however, the methods we present are also applicable to other types of systems. We focus on generic methods to approximate Resource Demands without relying on dedicated instrumentation of the application.
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FAS*W@SASO/ICAC - Utilizing Clustering to Optimize Resource Demand Estimation Approaches
2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2019Co-Authors: Johannes Grohmann, Nikolas Herbst, Simon Eismann, Andre Bauer, Marwin Zufle, Samuel KounevAbstract:Resource Demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these Resource Demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.
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ICAC - Self-Tuning Resource Demand Estimation
2017 IEEE International Conference on Autonomic Computing (ICAC), 2017Co-Authors: Johannes Grohmann, Simon Spinner, Nikolas Herbst, Samuel KounevAbstract:The average time a Resource needs to process incoming requests in a monitored workload mix is a key parameter of stochastic performance models. Direct measurement of these Resource Demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments.Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature each coming with different strengths and run-time overheads. Most approaches offer parameters in order to customize the behavior of the estimator influencing the estimation quality and the required computation time. However, their configuration usually requires exhaustive testing, as default parameters normally do not provide optimal performance.In this paper, we propose a self-tuning approach based on discrete optimization that can be used to automatically tune the parameters of Resource Demand estimation methods, tailoring them to the specific application scenario and thus improving their accuracy. We apply and compare different techniques on a representative data set with varying load levels and number of workload classes. We show that our selected approach for parameter tuning can automatically improve the estimation quality of certain estimators by up to 25%.
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comparing the accuracy of Resource Demand measurement and estimation techniques
Computer Performance Engineering - Proceedings of the 12th European Workshop (EPEW 2015), 2015Co-Authors: Felix Willnecker, Simon Spinner, Samuel Kounev, Markus Dlugi, Andreas Brunnert, Wolfgang Gottesheim, Helmut KrcmarAbstract:Resource Demands are a core aspect of performance models. They describe how an operation utilizes a Resource and therefore influence the systems performance metrics: response time, Resource utilization and throughput. Such Demands can be determined by two extraction classes: direct measurement or Demand estimation. Selecting the best suited technique depends on available tools, acceptable measurement overhead and the level of granularity necessary for the performance model. This work compares two direct measurement techniques and an adaptive estimation technique based on multiple statistical approaches to evaluate strengths and weaknesses of each technique. We conduct a series of experiments using the SPECjEnterprise2010 industry benchmark and an automatic performance model generator for architecture-level performance models based on the Palladio Component Model. To compare the techniques we conduct two experiments with different levels of granularity on a standalone system, followed by one experiment using a distributed SPECjEnterprise2010 deployment combining both extraction classes for generating a full-stack performance model.
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EPEW - Comparing the Accuracy of Resource Demand Measurement and Estimation Techniques
Computer Performance Engineering, 2015Co-Authors: Felix Willnecker, Simon Spinner, Samuel Kounev, Markus Dlugi, Andreas Brunnert, Wolfgang Gottesheim, Helmut KrcmarAbstract:Resource Demands are a core aspect of performance models. They describe how an operation utilizes a Resource and therefore influence the systems performance metrics: response time, Resource utilization and throughput. Such Demands can be determined by two extraction classes: direct measurement or Demand estimation. Selecting the best suited technique depends on available tools, acceptable measurement overhead and the level of granularity necessary for the performance model. This work compares two direct measurement techniques and an adaptive estimation technique based on multiple statistical approaches to evaluate strengths and weaknesses of each technique. We conduct a series of experiments using the SPECjEnterprise2010 industry benchmark and an automatic performance model generator for architecture-level performance models based on the Palladio Component Model. To compare the techniques we conduct two experiments with different levels of granularity on a standalone system, followed by one experiment using a distributed SPECjEnterprise2010 deployment combining both extraction classes for generating a full-stack performance model.
Qiufen Xia - One of the best experts on this subject based on the ideXlab platform.
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Efficient Embedding of Virtual Networks to Distributed Clouds via Exploring Periodic Resource Demands
IEEE Transactions on Cloud Computing, 2018Co-Authors: Weifa Liang, Qiufen XiaAbstract:Cloud computing built on virtualization technologies promises provisioning elastic computing and bandwidth Resource services for enterprises that outsource their IT services as virtual networks. To share the cloud Resources efficiently among different enterprise IT services, embedding their virtual networks into a distributed cloud that consists of multiple data centers, poses great challenges. Motivated by the fact that most virtual networks operate on long-term basis and have the characteristics of periodic Resource Demands, in this paper we study the virtual network embedding problem of embedding as many virtual networks as possible to a distributed cloud such that the revenue collected by the cloud service provider is maximized, while the service level agreements (SLAs) between enterprises and the cloud service provider are met. We first propose an efficient embedding algorithm for the problem, by incorporating a novel embedding metric that accurately models the dynamic workloads on both data centers and inter-data center links, provided that the periodic Resource Demands of each virtual network are given and all virtual networks have identical Resource Demand periods. We then show how to extend this algorithm for the problem when different virtual networks may have different Resource Demand periods. Furthermore, we also develop a prediction mechanism to predict the periodic Resource Demands of each virtual network if its Resource Demands are not given in advance. We finally evaluate the performance of the proposed algorithms through experimental simulation based on both synthetic and real network topologies. Experimental results demonstrate that the proposed algorithms outperform existing algorithms from $10$ to $31$ percent in terms of performance improvement.
Alison C. Cullen - One of the best experts on this subject based on the ideXlab platform.
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high severity wildfire potential associating meteorology climate Resource Demand and wildfire activity with preparedness levels
International Journal of Wildland Fire, 2021Co-Authors: Alison C. Cullen, Travis Axe, Harry PodschwitAbstract:National and regional preparedness level (PL) designations support decisions about wildfire risk management. Such decisions occur across the fire season and influence pre-positioning of Resources in areas of greatest fire potential, recall of personnel from off-duty status, requests for back-up Resources from other areas, responses to requests to share Resources with other regions during fire events, and decisions about fuel treatment and risk reduction, such as prescribed burning. In this paper, we assess the association between PLs assigned at national and regional (Northwest) scales and a set of predictors including meteorological and climate variables, wildfire activity and the mobilisation and allocation levels of fire suppression Resources. To better understand the implicit weighting applied to these factors in setting PLs, we discern the qualitative and quantitative factors associated with PL designations by statistical analysis of the historical record of PLs across a range of conditions. Our analysis constitutes an important step towards efforts to forecast PLs and to support the future projection and anticipation of firefighting Resource Demand, thereby aiding wildfire risk management, planning and preparedness.
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High-severity wildfire potential – associating meteorology, climate, Resource Demand and wildfire activity with preparedness levels
International Journal of Wildland Fire, 2021Co-Authors: Alison C. Cullen, Travis Axe, Harry PodschwitAbstract:National and regional preparedness level (PL) designations support decisions about wildfire risk management. Such decisions occur across the fire season and influence pre-positioning of Resources in areas of greatest fire potential, recall of personnel from off-duty status, requests for back-up Resources from other areas, responses to requests to share Resources with other regions during fire events, and decisions about fuel treatment and risk reduction, such as prescribed burning. In this paper, we assess the association between PLs assigned at national and regional (Northwest) scales and a set of predictors including meteorological and climate variables, wildfire activity and the mobilisation and allocation levels of fire suppression Resources. To better understand the implicit weighting applied to these factors in setting PLs, we discern the qualitative and quantitative factors associated with PL designations by statistical analysis of the historical record of PLs across a range of conditions. Our analysis constitutes an important step towards efforts to forecast PLs and to support the future projection and anticipation of firefighting Resource Demand, thereby aiding wildfire risk management, planning and preparedness.
