FACE‐IT: A science gateway for food security research

Montella, Raffaele; Kelly, David; Xiong, Wei; Brizius, Alison; Elliott, Joshua; Madduri, Ravi; Maheshwari, Ketan; Porter, Cheryl; Vilter, Peter; Wilde, Michael; Zhang, Meng; Foster, Ian

doi:10.1002/cpe.3540

Cited by 30 publications

(16 citation statements)

References 19 publications

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“…The issue of deploying services and applications on different environments has been addressed by traditional computational workflows [86], [108]- [111]. However, in practice, users have to perform troubleshooting procedures to deploy a workflow on a given infrastructure [112].…”

Section: Related Workmentioning

confidence: 99%

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

et al. 2020

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The edge, the fog, the cloud, and even the end-user's devices play a key role in the management of the health sensitive content/data lifecycle. However, the creation and management of solutions including multiple applications executed by multiple users in multiple environments (edge, the fog, and the cloud) to process multiple health repositories that, at the same time, fulfilling non-functional requirements (NFRs) represents a complex challenge for health care organizations. This paper presents the design, development, and implementation of an architectural model to create, on-demand, edge-fog-cloud processing structures to continuously handle big health data and, at the same time, to execute services for fulfilling NFRs. In this model, constructive and modular blocks, implemented as microservices and nanoservices, are recursively interconnected to create edge-fog-cloud processing structures as infrastructureagnostic services. Continuity schemes create dataflows through the blocks of edge-fog-cloud structures and enforce, in an implicit manner, the fulfillment of NFRs for data arriving and departing to/from each block of each edge-fog-cloud structure. To show the feasibility of this model, a prototype was built using this model, which was evaluated in a case study based on the processing of health data for supporting critical decision-making procedures in remote patient monitoring. This study considered scenarios where end-users and medical staff received insights discovered when processing electrocardiograms (ECGs) produced by sensors in wireless IoT devices as well as where physicians received patient records (spirometry studies, ECGs and tomography images) and warnings raised when online analyzing and identifying anomalies in the analyzed ECG data. A scenario where organizations manage multiple simultaneous each edge-fog-cloud structure for processing of health data and contents delivered to internal and external staff was also studied. The evaluation of these scenarios showed the feasibility of applying this model to the building of solutions interconnecting multiple services/applications managing big health data through different environments. INDEX TERMS Big health data, edge-fog-cloud, health-IoT processing, Internet of Things, microservice architecture.

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Section: Related Workmentioning

confidence: 99%

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

et al. 2020

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“…There is the possibility to indicate suitable areas for maize planting based on a GIS system approach in combination with multicriteria evaluation by using hyper-resolution data of climate (precipitation and temperature) and soil conditions, as well as topography [63]. FACE-IT (Framework to Advance Climate, Economic, and Impact Investigations with IT) may support decisions by providing easy access to data, simulation models, and analysis tools [64].…”

Section: Challengesmentioning

confidence: 99%

Evaluation of Sustainability of Maize Cultivation in Poland. A Prospect Theory—PROMETHEE Approach

et al. 2018

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This study aims at exploiting research outcomes concerning tillage practices in order to make solutions available to farmers to mitigate negative environmental impact to soils. Two alternative practices have been analysed against conventional full tillage based on data provided by a long-term experiment conducted at the Institute of Soil Science and Plant Cultivation (IUNG) farm in Grabów, Central Poland. Reduced Tillage and Direct Sowing are evaluated against Full Tillage on the basis of socio-economic and environmental criteria. Multi-criteria decision analysis undertaken using the PROMETHEE method provided evidence that the ‘optimal’ maize cultivation system depends on the decision maker’s viewpoint and preferences. In fact, criteria selected and related weights elicited from representative farmers, as well as from an expert agronomist, reveal different viewpoints. Direct sowing was the most preferable for the large farm and expert perspective, whereas in case of small farm reduced tillage ranked first. Prospect theory developed by behavioural economists was incorporated to take into account decision biases. As a matter of fact, based on Prospect Theory-PROMETHEE from the small farm and the expert perspective, the conventional system was now ranked first, while for the large farm, the most preferable practice was still direct sowing.

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“…Its development is contextualized in the effort for a robust, stable, and consistent operational forecast system to protect mussel farms from pollution, which is one of the outstanding issues in many developing world economies. The numerical models, with a specific parametrization and spatial/temporal resolution, have been integrated in the framework to advance climate, economic, and impact investigations with information technology work‐flow system, providing support for experiment repeatability and interactive data publishing. () WaComM uses a hybrid approach, based on Eulerian‐Lagrangian models, implemented using a heterogeneous parallel approach .…”

Section: Real Use Case Scenariosmentioning

confidence: 99%

Accelerating Linux and Android applications on low‐power devices through remote GPGPU offloading

Montella

Kosta

Oro³

et al. 2017

Concurrency and Computation

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Summary Low‐power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real‐time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low‐powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance, a commercial real‐time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low‐power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer.

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FACE‐IT: A science gateway for food security research

Cited by 30 publications

References 19 publications

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

Evaluation of Sustainability of Maize Cultivation in Poland. A Prospect Theory—PROMETHEE Approach

Accelerating Linux and Android applications on low‐power devices through remote GPGPU offloading

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