Elsevier

Future Generation Computer Systems

Volume 110, September 2020, Pages 453-467
Future Generation Computer Systems

Using the FACE-IT portal and workflow engine for operational food quality prediction and assessment: An application to mussel farms monitoring in the Bay of Napoli, Italy

https://doi.org/10.1016/j.future.2018.03.002Get rights and content

Highlights

  • A FACE-IT Galaxy Globus application implementing operational food quality prediction and assessment.

  • Design and implementation of FACE-IT Galaxy Globus components for earth science workflow application development.

  • Weather, ocean circulation and pollutant transport/diffusion model workflow tool wrapping for offline coupling.

Abstract

The Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) is a workflow engine and data science portal based on Galaxy and Globus technologies that enables computational scientists to integrate data, pre/post processing and simulation into a framework that supports offline environmental model coupling. We describe how the FACE-IT workflows engine can be used to couple many simulation/prediction models, leveraging high-performance cloud computing resources to enable fast full system modeling and produce operational predictions about the impact of pollutants spilled out from both natural and anthropic sources in mussels farming high density areas. Mussel farms product quality remains a challenging problem for operational marine science: in this scenario, the model chain presented in this work, orchestrated in a workflow fashion, produces a huge amount of predicted spatially-referenced (big) data. The software infrastructure we built using FACE-IT Galaxy Globus provides tools enabled to evaluate the impact of hazardous substances (chemical or biological) continuously or spottily spilled in the marine environment.

Introduction

In recent decades coastal areas have become more vulnerable. Population growth has led to the rapid expansion of coastal settlements, aquaculture, fishery, mussel farming and agriculture in coastal areas. Global climate change effects cause sea level rise, warming waters and changes in storm patterns; pollutants due to natural and/or anthropogenic impacts further destabilize such regions. As scientists and operators work towards the co-existence of coastal resource exploitation and smart environment management, fishery and mussel farm quality is an important issue in operational computational marine science.

Projections of future food quality require data from climate models to forecast future conditions, coupling weather models, wind-driven sea waves models and ocean circulation/river advection models, evaluations of transport and diffusion of pollutants on projections about future infrastructures, such as new fishery and mussel farm installments, land-use and land cover. Decisions are mainly made by coastal management planners in designing or re-designing human facilities, sea/lake fronts, ports, harbors and farms (fishery, mussels) placement using scenario analysis tools.

A tactical, on-demand application for operational fishery and mussel farms would produce food quality predictions and assessments to enable scientists and technical operators in short-term (smart) city management to cooperate in order to obtain the best results from real-time space and temporal high-resolution weather forecasts, dynamically integrating data from prediction models of weather, wind-driven sea waves, sea currents, flooding and pollution diffusion and dispersion in both air and water [1]. The decisions, made by city/area managers, would be supported by on-demand analysis tools (Fig. 1).

Such a system would require:

  • the management of diffuse and point pollution sources;

  • the ability to scale in terms of domain size;

  • the computational performance and effectiveness needed to provide results for decision support.

Moreover, human health can be adversely affected by emission of pollutants into sea water that contaminate seafood. It is important to improve the evaluation of pollution effects in aquatic ecosystems, both for an economic profit and to improve the environmental sustainability.

We describe here a real world operational and on-demand application for mussel farm food quality prediction and assessment.

Users (both field scientists and food quality/human health managers and experts) interact with the FACE-IT Galaxy data portal [2] in order to evaluate the ongoing situation, generate alerts and depict future scenarios for strategic management (http://www.faceit-portal.org). While the weather and the ocean circulation models wrapped as workflow tools are well known [3], widely used and community supported, the pollutant transport and dispersion model has been developed specifically for this application and designed to be integrated in the FACE-IT Galaxy workflow.

The rest of this paper is as follows: Section 2 discusses the contextualization and motivation of the paper; Section 3 introduces the general FACE-IT infrastructure and how it has been developed in the context of agricultural modeling and food quality and extended in order to support the described application; Section 4 details the application workflow and how different models have been implemented to fit the proposed workflow infrastructure; Section 5 discusses computational and environmental issues as carried out from data analysis; Section 6 introduce the related work and finally Section 7 presents conclusions and proposes future work.

Section snippets

Contextualization and motivation

Direct acyclic graph-fashioned job managers and workflow engines [4] are first class citizens in the world of high-end computing since decades as demonstrated by Condor DAGMan [5], Unicore [6] and, more recently, Swift [7]. At the design stage of the proposed system for operational food quality prediction and assessment we considered some of the most notable currently available general scientific workflow environments:

  • Kepler is a scientific workflow environment with the main aim of helping

The FACE-IT infrastructure

Sustainability Science examines the interaction between human, environment and engineered systems with the aim to find solutions, supported by numerical acquisitions and simulations, for complex challenges that often threaten the human health. The scientific progress is hindered by the implementation and management of complex pipelines dedicated to managing the phases of data acquisition, pre-processing, simulations and post processing.

In this context, we implemented the integrated data

Tools and workflows

Improving the evaluation of pollution effects in aquatic ecosystems is important for economic profit gathered by business like professional fishing, aquaculture and tourism, for enforcing environmental sustainability and for improving overall quality of human health.

To achieve this target we implemented a decision making tool, based on numerically coupled models that can forecast atmospheric and marine dynamics designed to comply with the following issues:

  • Based on open source components and

Computational environment

The FACE-IT application on mussel farms modeling for food quality assessment and human health protection has practical applications from both computational and environmental points of view.

Related work

The atmosphere model is the main data producer in the presented application. The Weather Research and Forecasting (WRF) [27] system contains two dynamical solvers, referred to as the ARW (Advanced Research WRF) core and the NMM (Nonhydrostatic Mesoscale Model) core. As stressed by many authors in [43] and in [44], the two cores differs for the aspects related to the governing equations, prognostic variables, horizontal grid, vertical grid, terrain formulation, time integration method and

Conclusions

We described our use of the Globus Galaxy-based FACE-IT technology in a project that extends FACE-IT’s initial focus on climate, agricultural and socio-economic interactions to a tactical pollutant prediction application. The improvements to the FACE-IT infrastructure needed to support this new domain included new NetCDF Schema-based datatypes; new tool map parameters for date/time, map, and OPeNDAP handling; and, last but not the least, the implementation of interactive map support for both

Acknowledgments

We thank the Globus Galaxies, Globus, and Galaxy teams for their outstanding work on those systems and for their assistance with this project. EC2 resources were generously provided by Amazon. The microbiological testing have been carried out by Istituto Zooprofilattico Sperimentale del Mezzogiorno (Portici, Naples, Italy).

This work has been supported in part by the RDCEP, Center for Robust Decisionmaking on Climate and Energy Policy, (NSF cyberSEES program award No. 1331782, USA); in part by

Raffaele Montella works as assistant professor, with tenure, in Computer Science at Department of Science and Technology, University of Naples “Parthenope”, Italy since 2005. He was visiting assistant professor in Computer Science at the University of Chicago in 2015/2016. He got his degree (M.Sc. equivalent) in (Marine) Environmental Science at the University of Naples “Parthenope” in 1998 defending a thesis about the “Development of a GIS system for marine applications” scoring with laude and

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    Raffaele Montella works as assistant professor, with tenure, in Computer Science at Department of Science and Technology, University of Naples “Parthenope”, Italy since 2005. He was visiting assistant professor in Computer Science at the University of Chicago in 2015/2016. He got his degree (M.Sc. equivalent) in (Marine) Environmental Science at the University of Naples “Parthenope” in 1998 defending a thesis about the “Development of a GIS system for marine applications” scoring with laude and an award mention to his study career. He defended his Ph.D. thesis about “Environmental modeling and Grid Computing techniques” earning the Ph.D. in Marine Science and Engineering at the University of Naples Federico II. The research main topics and the scientific production are focused on tools for high performance computing, such as grid, cloud and GPUs with applications in the field of computational environmental science (multidimensional big data/distributed computing for modeling and scientific workflows and science gateways) leveraging on his previous (and still ongoing) experiences in embedded/mobile/wearable/pervasive computing and internet of things.

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