Running high resolution coastal models in forecast systems: Moving from workstations and HPC cluster to cloud resources

Rogeiro, João; Rodrigues, Marta; Azevedo, Alberto; Oliveira, Anabela; Martins, J.P.; David, M.; Pina, J.; Dias, Nuno; Gomes, Jorge

doi:10.1016/j.advengsoft.2017.04.002

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…The UBEST forecasts are performed daily, taking advantage of an established model and procedures to reliably and consistently produce the predictions. These procedures are as-sembled in the forecast engine, a software built on top of the Water Information Forecast Framework (WIFF, Fortunato et al, 2017a;Rogeiro et al, 2018), a Python-based framework, that is also used to enable the on-demand forecast systems in the OPENCoastS service (https://opencoasts.ncg.ingrid.pt, Oliveira et al, 2020). WIFF provides most of the generic aspects required to operate a forecast system, such as coupling different simulation models, retrieving the necessary forcings, running simulations remotely or locally with MPI, chaining periodic simulations to make them a continuous series of events, postprocessing the model results and providing notifications to the users and administrators about progress or unexpected behavior.…”

Section: Forecast Enginementioning

confidence: 99%

A Web-Based Observatory for Biogeochemical Assessment in Coastal Regions

Rodrigues¹,

Martins²,

Rogeiro³

et al. 2021

J ENVIRON INFORM

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The concept of water observatories is extended to create a highly versatile tool for both the daily and the long-term management of estuarine ecosystems. Coastal observatories are evolving from simple data repositories to include forecasts, scenarios' analyses and indicators, integrated in web platforms that provide multiple products and services. In a context of climate change (CC) and growing anthropogenic pressures, the assessment of the ecological health implies that the biogeochemical status is adequately quantified and incorporated in the coastal management decision-making procedure. This quantification requires accurate models for hydrodynamics and ecology that account for all relevant processes at the right scales. These models must be applied in forecast mode for emergency purposes and in hindcast mode to explore multiple scenarios as part of the CC adaptation strategy, creating a complex, vast amount of information to be shared with the coastal managers. A web-based portal supported by a comprehensive modeling and forecasting framework and materialized along the main water quality/biogeochemistry themes, from data to indicators, is developed and demonstrated in two distinct yet complex coastal systems: the Tagus estuary and the Ria Formosa lagoon. The paper starts with the requirements analysis from both ecological and computer science perspectives and then presents the overall multi-layered architecture of the framework and its key software components. The observatory portal implementation and demonstration explore its usefulness for coastal management.

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Section: Forecast Enginementioning

confidence: 99%

A Web-Based Observatory for Biogeochemical Assessment in Coastal Regions

Rodrigues¹,

Martins²,

Rogeiro³

et al. 2021

J ENVIRON INFORM

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show abstract

“…For selected observatories, model predictions can be generated using the OPENCoastS forecast service [20]. This service can be used to forecast the circulation in any coastal area worldwide at the choice of the user, supported by the Water Information Forecast Framework (WIFF, [12,21]) and the European Open Science Cloud resources (https://www. eosc-hub.eu/ (accessed on 10 May 2021)).…”

Section: Modeling and Forecasting Infrastructurementioning

confidence: 99%

“…Regional Forcings for the Observatory Predictions Both regional wave and storm surge (plus tidal) forecasts are provided in the webpage regional forcings (forç amentos regionais). Based on a 10-year-old public LNEC service for forecasting water dynamics on the Portuguese coast and the North Atlantic (http://ariel.lnec.pt) using the WIFF-Water Information Forecast Framework ( [12,21]), the user can visualize the wave variables and water level for the next 48 h and thus anticipate any events that are generated offshore. The user is allowed to choose a date (post-January 2019) by using a calendar widget and a prediction (previsã o) type variable to trigger the showcasing of the data.…”

Section: Historical Analysis Of Past Flooding and Overtopping Eventsmentioning

confidence: 99%

Multi-Hazard WebGIS Platform for Coastal Regions

et al. 2021

Self Cite

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The combined action of waves, surges and tides can cause flooding, erosion and dune and structure overtopping in many coastal regions. Addressing emergency and risk management in these areas require a combination of targeted campaigns and real-time data that measure all phenomena at stake and can be used to develop comprehensive monitoring platforms. These monitoring platforms can support the development of prediction tools that address all hazards in an integrated way. Herein, we present a methodology focused on multi-hazard coastal alert and risk, and its implementation in a tailored WebGIS platform. The MOSAIC platform offers a one-stop-shop capacity to access in-situ and remote sensing data, and hydrodynamic and morphodynamic predictions, supported by numerical models: SCHISM and XBeach. Information is structured on a local observatory scale, with regional forcings available for the correct interpretation of local hazards effects. This implementation can be further applied and extended to other coastal zones. The MOSAIC platform also provides access to a detailed database of past hazardous events, organized along several risk indicators, for the western coast of Portugal. The combination of features in the platform provides a unique repository of hazard information to support end-users for both emergency and long term risk planning actions.

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“…Besides, PSE tasks are characterized by being totally independent, which means they can be executed in a parallel way. Because of this, PSEs are considered suitable for distributed infrastructures, such as those provided by public clouds [3][4][5]. In this sense, by executing these applications in cloud environments, the speedup that can be achieved is signifcant.…”

Section: Introductionmentioning

confidence: 99%

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

Yannibelli

Pacini

Monge

et al. 2023

Scientific Programming

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Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. These applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that offer instances of different VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends significantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral differences with NSGA-III. Then, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering different application sizes. To do that, we use the well-known CloudSim simulator and consider different VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and significant savings in terms of computing time (10%–17%), monetary cost (10%–40%), and spot instance interruptions (33%–100%).

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Running high resolution coastal models in forecast systems: Moving from workstations and HPC cluster to cloud resources

Cited by 8 publications

References 14 publications

A Web-Based Observatory for Biogeochemical Assessment in Coastal Regions

A Web-Based Observatory for Biogeochemical Assessment in Coastal Regions

Multi-Hazard WebGIS Platform for Coastal Regions

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

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