مقاله انگلیسی رایگان در مورد ترکیب رایانش لبه ای و ابری برای تحلیل متاژنومیکس – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله ترکیب رایانش لبه ای و ابری برای تحلیل متاژنومیکس کم قدرت و مقرون به صرفه
عنوان انگلیسی مقاله Combining Edge and Cloud Computing for Low-Power, Cost-Effective Metagenomics Analysis
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۲۷ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF) ۴٫۶۳۹ در سال ۲۰۱۷
شاخص H_index ۸۵ در سال ۲۰۱۹
شاخص SJR ۰٫۸۴۴ در سال ۲۰۱۹
رشته های مرتبط کامپیوتر، فناوری اطلاعات
گرایش های مرتبط رایانش ابری، هوش مصنوعی، شبکه های کامپیوتری، اینترنت و شبکه های گسترده
نوع ارائه مقاله ژورنال
مجله / کنفرانس نسل آینده سیستم های کامپیوتری – Future Generation Computer Systems
دانشگاه National Research Council of Italy – Genoa – Italy
کلمات کلیدی متاژنومیکس؛ ژنومیک محیطی؛ محاسبات لبه؛ محاسبات ابری؛ اینترنت اشیا؛ اینترنت اشیا زندگی
کلمات کلیدی انگلیسی Metagenomics; Environmental genomics; Edge computing; Cloud computing; Internet of Things; Internet of Living Things
شناسه دیجیتال – doi
https://doi.org/10.1016/j.future.2018.07.036
کد محصول E9424
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱٫ Introduction
۲٫ The hardware of the device
۳٫ The metagenomic analysis workflow
۴٫ Experimental results
۵٫ Conclusion and future development
References

بخشی از متن مقاله:
Abstract

Metagenomic studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial to aquatic ecosystems. This also because genome sequencing is likely to become a routinely and ubiquitous analysis in a near future thanks to a new generation of portable devices, such as the Oxford Nanopore MinION. The main issue is however represented by the huge amount of data produced by these devices, whose management is actually challenging considering the resources required for an efficient data transfer and processing. In this paper we discuss these aspects, and in particular how it is possible to couple Edge and Cloud computing in order to manage the full analysis pipeline. In general, a proper scheduling of the computational services between the data center and smart devices equipped with low-power processors represents an effective solution.

Introduction

Genome sequencing is one of the most effective analysis technique to monitor both the human body, in physiological settings and pathological conditions, as well as the bacterial communities of different environments. Developed in the 1970s with a cost of hundred million dollars, its impressive progress [1] reduced the cost down to about $1000 dollars, and the perspective is a further reduction to about $100 for genome1 . In particular, the MinION by Oxford Nanopore [2], a miniaturized sequencing instrument device with a weight under 100g powered by its USB port, represents one of the most promising tool belonging to the thirdgeneration DNA sequencing technology [3]. Coupled with a laptop, MinION can be used on the field [4] to obtain genomic sequences, thus providing essential information for tracing back the organisms present in the environment [5]. These devices have been widely used for microbiology studies 2 , for water monitoring3 and in agriculture 4 . Portable sequencer can also be used to monitor bacteria in air-filters of hospitals, food industries, and pharmaceutical companies in order to give alarms in case pathogens are identified [6, 7]. More extreme usages of Oxford Nanopore devices have also been experimented [8, 9, 10]. This new trend is sometimes referred as Internet of Living Things (IoLT) [11]. The combination of the MinION sequencers with remote platform for data integration is still in its infancy, although some attempts have been reported in conference talks and blogs [12, 13]. Moreover, some prototypes of IoT platforms for monitoring clean water [14, 15], precision farming [16, 17, 18], livestock [19, 20] and more generally to improve agricultural productivity [21, 22, 23] have been presented. The two major obstacles affecting the utility of this kind of devices are the access to suitable computational capabilities and bandwidth, since in principle it is possible to stream a couple of gigabytes of raw data per day. Even if the raw data analysis software works reliably on most of the current laptops, it is a very resource-intensive task. The adoption of a Cloud-based approach can mitigate such issue, though it comes at the expense of broadband connections.

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