Combining Edge and Cloud computing for low-power, cost-effective metagenomics analysis

D’Agostino, Daniele; Morganti, Lucia; Corni, Elena; Cesini, Daniele; Merelli, Ivan

doi:10.1016/j.future.2018.07.036

Cited by 37 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a previous work by Merelli et al, the authors demonstrated the feasibility and advantages of executing such a metagenomic analysis in SoCs within an edge‐fog computing scenario, which is true also for some compute‐intensive applications, such as the N‐Body algorithm, when considering performance, energy consumption, and economical aspects altogether …”

Section: Case‐study: Metagenomic Analysis On Low‐power Socsmentioning

confidence: 93%

On low‐power SoCs as storage bricks for Bioinformatics

Morganti

Corni

Lama

et al. 2019

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

In the perspective of energy-efficient storage solutions supporting scientific computing, we investigate the possibility of using low-power Systems-On-Chip (SoCs) as storage bricks of a BeeGFS file system in support of a realistic genome sequencing pipeline from Bioinformatics.Metadata and data performances of such a file system made of low-power devices are assessed via various benchmarks for an increasing number of clients accessing the data, and compared to those achieved with traditional power-hungry architectures with a similar BeeGFS setup. Then, we show how the sequencing information streamed by portable sequencing devices, such as the Oxford Nanopore MinION, can be managed by the underlying BeeGFS file system built with low-power SoCs.

show abstract

Section: Case‐study: Metagenomic Analysis On Low‐power Socsmentioning

confidence: 93%

On low‐power SoCs as storage bricks for Bioinformatics

Morganti

Corni

Lama

et al. 2019

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The possibility of using low power system‐on‐chips (SoCs) for scientific applications, which are traditionally executed in HPC environments, is gaining an increasing attention in view of greener computing, power and money savings, energy efficiency, and reduction of infrastructural sizes . This is particularly true for Fog/edge computing architectures …”

Section: Themes Of This Special Issuementioning

confidence: 99%

Latest advances in parallel, distributed, and network‐based processing

Merelli

Lió

Kotenko

et al. 2020

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

This editorial introduces the articles selected for the special issue concerning the International Conferences on Parallel, Distributed, and Network-Based Processing, which provided insights related to the efficient exploitation of parallel and distributed architectures, including power-aware computing, application scheduling, and application development for GPUs. K E Y W O R D SComputing, gpu computing, network-based computing, parallel Computing

show abstract

“…The choice of placement depends generally on application latency, sensitivity, availability of resources, analysis task complexity, network bandwidth and security. Recent works propose placement on both locations [ 17 ] for better resource utilization and optimization. In some IoT systems, sensors and gateways are deployed in harsh environments and can hardly conserve their energy for long periods.…”

Section: Introductionmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

View full text Add to dashboard Cite

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

show abstract

Combining Edge and Cloud computing for low-power, cost-effective metagenomics analysis

Cited by 37 publications

References 34 publications

On low‐power SoCs as storage bricks for Bioinformatics

On low‐power SoCs as storage bricks for Bioinformatics

Latest advances in parallel, distributed, and network‐based processing

Efficient Anomaly Detection for Smart Hospital IoT Systems

Contact Info

Product

Resources

About