Cloud Technology for Forecasting Accuracy Evaluation of Extreme Metocean Events

Kosukhin, Sergey S.; Kovalchuk, Sergey V.; Boukhanovsky, Alexander V.

doi:10.1016/j.procs.2015.05.483

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Workflows in both categories may exhibit multiple deadlines, but our concern in this paper is with the latter kind of workflow, which are often used for time critical applications in environmental monitoring. UrbanFlood [6] is an example of an early warning system that tries to solve the problem of flood control, while Kosukhin [7] presents an architecture for performing extreme metocean event forecasting on cloud platforms. In the case of the UrbanFlood system, the workflow has multiple stages with separated modules for sensor monitoring, AI anomaly detection, reliability analysis, breach simulation, virtual dikes, and decision support.…”

Section: Related Workmentioning

confidence: 99%

Planning virtual infrastructures for time critical applications with multiple deadline constraints

Wang

Taal

Martin

et al. 2017

Future Generation Computer Systems

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Executing time critical applications within cloud environments while satisfying execution deadlines and response time requirements is challenging due to the difficulty of securing guaranteed performance from the underlying virtual infrastructure. Cost-effective solutions for hosting such applications in the Cloud require careful selection of cloud resources and efficient scheduling of individual tasks. Existing solutions for provisioning infrastructures for time constrained applications are typically based on a single global deadline. Many time critical applications however have multiple internal time constraints when responding to new input. In this paper we propose a cloud infrastructure planning algorithm that accounts for multiple overlapping internal deadlines on sets of tasks within an application workflow. In order to better compare with existing work, we adapted the IC-PCP algorithm and then compared it with our own algorithm using a large set of workflows generated at different scales with different execution profiles and deadlines. Our results show that the proposed algorithm can satisfy all overlapping deadline constraints where possible given the resources available, and do so with consistently lower host cost in comparison with IC-PCP.

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Section: Related Workmentioning

confidence: 99%

Planning virtual infrastructures for time critical applications with multiple deadline constraints

Wang

Taal

Martin

et al. 2017

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Further, the tasks are distributed among the storage agents, which perform the direct local launch of the applications. Thus, the cloud computing solution, which is discussed in more details in [45], allows performing an automatic intelligent coupling of models and data sources to take into account all elements of a particular ensemble, especially under the condition when a collection of integrated applied software is regularly expanded.…”

Section: Figure 6 Conservative Selection With Predefined Thresholdmentioning

confidence: 99%

Classification issues within ensemble-based simulation: application to surge floods forecasting

Kovalchuk

Krikunov

Knyazkov

et al. 2016

Stoch Environ Res Risk Assess

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Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty management is ensemble-based simulation, which uses variation in input or output data, model parameters, or available versions of models to improve the simulation performance. Furthermore, the system of models for complex system simulation (especially in case of hiring ensemble-based approach) can be considered as a complex system. As a result, the identification of the complex model's structure and parameters provide additional sources of uncertainty to be managed. Within the presented work, we are developing a conceptual and technological approach to manage the ensemblebased simulation taking into account changing states of both simulated system and system of models within the ensemble-based approach. The states of these systems are considered as a subject of classification with consequent inference of better strategies for ensemble evolution over the simulation time and ensemble aggregation. Here the ensemble evolution enables implementation of dynamic reactive solutions that can automatically conform to the changing states of both systems. The ensemble aggregation can be considered within a scope of averaging (regression way) or selection (classification way, which complement the classification mentioned earlier) approach. The technological basis for such approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessment, etc.); and machine learning algorithms for classes identification, ensemble management and knowledge acquisition. Within the work, a set of case studies is addressed to examine the opportunities provided by the developed approach: metocean events' forecasting simulation, urban traffic environment, multi-agent crowd simulation, etc.

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Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin

Noymanee

Nikitin

Kalyuzhnaya

2017

Procedia Computer Science

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Cloud Technology for Forecasting Accuracy Evaluation of Extreme Metocean Events

Cited by 4 publications

References 13 publications

Planning virtual infrastructures for time critical applications with multiple deadline constraints

Planning virtual infrastructures for time critical applications with multiple deadline constraints

Classification issues within ensemble-based simulation: application to surge floods forecasting

Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin

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