Universidad Nacional de Chimborazo

NOVASINERGIA, 2018, Vol. 1, No. 2, junio-noviembre, (70-82)

ISSN: 2631-2654

https://doi.org/10.37135/unach.ns.001.02.08

Research Article

A Fog Assisted Cloud Paradigm for accessibility and collaboration to

Genomic Data Analysis

Fog Asiste Cloud Paradigma para la accesibilidad y colaboraci ´on al An ´alisis de

Datos Gen ´omicos

Increasingly growing Next-generation sequencing requires large-scale computing

resources to handle the huge amount of data produced. The Cloud computing paradigm

readily handles huge data but the core issue with this paradigm is transfer of enormous

data to and from cloud computers due to limited bandwidth which lies in the centralized

nature of a Cloud computing architecture that is located far away from users. An

architecture where computing power is distributed more evenly throughout the network

is the way to combat this problem. The architecture should drive the processing capacity

describes the Co-Fog model that promises increased performance, energy efficiency,

reduced latency, faster response time, scalability, and better localized accuracy for

future large-scale collaborations in genomics.

Big data, Distributed resource management, Cloud computing, Fog computing,

Next-generation sequencing (NGS)

Resumen:

La secuenciaci ´on de la pr ´oxima generaci ´on es cada vez m

´

as creciente y requiere

recursos inform ´aticos a gran escala para manejar la enorme cantidad de datos

arquitectura debe llevar la capacidad de procesamiento hacia el borde de la red, m ´as

cerca de la fuente de los datos. Para esta propuesta, Fog computing ofrece una soluci ´on

prometedora para acercar las capacidades computacionales a los datos generados y

ser ´a la soluci ´on para ganar fuerza en la investigaci ´on gen ´omica. Proponemos un nuevo

modelo llamado Collaborative-Fog (Co-Fog) que adopta los paradigmas Fog y Cloud

computing para administrar grandes conjuntos de datos gen ´omicos y para permitir

la comprensi ´on de c ´omo las partes interesadas pueden gestionar la interacci ´on y la

colaboraci ´on. El presente trabajo describe el modelo Co-Fog que promete un mayor

rendimiento, eficiencia energ ´etica, menor latencia, tiempo de respuesta m ´as r ´apido,

escalabilidad y una mejor precisi ´on localizada para futuras colaboraciones a gran

Genomic medicine is an emerging medical

discipline that uses the human genomic data

of an individual as a part of their clinical care.

Besides healthcare, genomic data is used in various

domains, including research on genomic-wide

association studies, ancestry determination, legal

cases (e.g., paternity cases), and forensic and

criminal investigations. The potential of genomics

could revolutionize clinical care by providing

and development and implementation of genomic

medicine based on genomic data analysis has

tremendous economic potentials, which indeed

explains the success of many enterprises such

as 23andMe, Color Genomics and some recent

services from Google Genomics, IBM Watson,

Microsoft Genomics, Amazon AWS Genomics and

Apple Research Kit.

Thus the recent advances in genomic research are

leading to a new era in medicine. In the next few

of personalized diagnoses and treatment based

on genomic data analysis and genomic medicine

seems just around the corner, the study of genomics

is becoming a field that is dominated by the growth

of data.

Below, we identify the useful collections of

publicly available next-generation sequencing

(NGS) datasets and how new advances in NGS

technologies are greatly expanding the current

volume and the range of existing data. The

the European Bioinformatics Institute (EBI)

and the DNA Data Bank of Japan (DDBJ). The

mission of INSDC is to preserve public-domain

sequencing data and to provide free, unrestricted

and permanent access to the data. By the

Figure 1: An overview of the publicly available data at the

Sequence Read Archive (SRA) based on user-submitted

metadata. The SRA’s growth rate is greatly increasing

over time. Half of the SRA submissions have been

generated by genomic library strategies, mostly whole

(figure 1). However, that volume of data represents

only the tip of the iceberg as the number of

genomic submissions has been steadily increasing,

particularly in recent years.

Huge amounts of genomic data requires high

performance computing and data storage

infrastructure that is often beyond the capabilities

of single institution. For this reason, Cloud

computing is becoming the preferred solution for

medical research centers and healthcare providers

genomic data in flexible and cost-effective way.

Cloud computing facilitates the storage and

management of large amount of data, acts as a final

destination for heavy-weight processing, long-term

storage and analysis. Cloud computing eliminates

cost for development and maintenance. Challenges

of using Cloud computing for genomic data include

lengthy data transfers for uploading data to the

cloud server, the perceived lack of information

safety in Cloud computing, and the requirement for

developers with advanced programming skills.

the cloud is growing, there is undue pressure on the

cloud infrastructure. Due to the loosely controlled

and non-homogeneous nature of the internet there

are several other issues related to Cloud computing

that are still unresolved. One such issue is network

delay or lag between client request and cloud

response for real time applications. This can be

explained by fact that data centers are often located

far away from major cities and population areas.

This physical distance between data centers and

motivation and application of fog assisted cloud

Collaborative-Fog (Co-Fog) paradigm. We finally

conclude with conclusions and future research

opportunities.

Cloud and Fog computing are two standing-alone

technological paradigms under the real of the Future

Internet. The National Institute for Standards and

service provider interaction. Cloud computing

provides the tools and technologies to build

data/compute intensive parallel applications

with much more affordable prices compared

to traditional parallel computing techniques.

X Scalability

X Network resource preservation

X Close loop control

X Resilience

X Clustering

7 7 7

Cloud computing offers higher-level services,

unlike local server or a personal computer that

can be rapidly provisioned and released with

minimal management effort and relies on the

self-establishment and self-management in order

to guarantee scalability to large scale (Mell &

Grance, 2011; Armbrust et al., 2010; Fox et al.,

2009; Buyya et al., 2009). Cloud providers deliver

PaaS providers enable users to access a platform to

develop and deploy software, and; iii) Software as a

Service (SaaS): SaaS users access software running

and management of large amount of data and can

serve as a possible instrument of surveillance. The

clouds act as the final destination for heavy-weight

processing, long-term storage and analysis.

Fog computing conceptual model defines Fog

resource paradigm that resides between end devices

and Cloud computing data centers. This paradigm

supports vertically-isolated, latency-sensitive

applications by providing ubiquitous, scalable,

layered, and federated distribution of the

communication, computation, and storage

resources. Precisely, fog nodes (physical (e.g.

gateways, switches, routers, servers, etc.) or

virtual components (e.g. virtualized switches,

virtual machines, cloudlets, etc.)) are small-size

of genomic data generated following NGS. It was

recognized that data storage/management is a

growing problem which will require Cloud and Fog

technology (Charlebois et al., 2016).

For science users, Fog computing is having two

main advantages: reproducibility and local/global

features for coping with the aforementioned

technological issues. The Fog computing

architecture consisting of three components,

namely: IoT nodes, fog nodes and back-end

cloud, where fog nodes are small-size virtualized

inter-connected resource-equipped data centers, that

process tasks without third-party interference and

collaboratively provide computational flexibility,

better communication, storage capacity, and much

more additional new smart services in a hierarchical

So doing, the native resources of the physical things

could be employed only for the synchronization

with the corresponding Fog-hosted clones, allowing

to the users, perhaps on opposite ends of the globe,

to create near-identical hardware and software

setups (see figure 3), where the reproducibility

using Virtual Machine (VM)-based technology

and the (emerging) container based technology,

which are tools that package software with all

the necessary components to enable reproducible

deployment in different computing environments.

3 that sketches the reference architecture of the

Big Data (BD) technological platform. It is

composed of five blocks, which are: 1) the

IoT (data generation) layer; 2) the radio access

network; 3) the proximate Fog layer; 4) the

Figure 3: Reference architecture for the Big Data

scenario.

local pre-processing (like, data compressing, fusion

and filtering), and; iii) routed to cloud-based remote

data centers over (multi-hop) internet WANs for

further post-processing.

The goal is to allow users to share information

in real-time by leveraging Fog-assisted social

network platforms (like Globus Genomic, Dropbox,

iCloud). These applications require massive sets of

inter-stream cross-correlation analytics, to quickly

detect the occurrence of new social trends and/or

decisions over the type of services and deployment

models influence the form Fog computing adoption

and reflect how varying ethical, legal and social

challenges are managed.

service where genetic sequence information

is stored and processed virtually, usually via

networked, large-scale data centers accessible

remotely through various users and platforms over

the internet” (Dove et al., 2015).

The following illustrative example (see figure

Fog-over-Users interplay and the roles played

by the Computer-to-Computer (C2C) interaction

model, in order to establish collaboration, links,

and ties.

In the figure 4 we show the main building

blocks and involved users, where the fog fosters

reproducibility by enabling investigators (users)

to publish data-sets (resp., User A (Data A) and

User B (Data B)) in figure 4) to the fog/cloud,

including different versions thereof, without loss or

communication, and inter-clone communication

(see the green paths of figure 4). So, it is expected

processing strategies and features (virtualization)

and makes extendable nontrivial computation

services.

The Fog computing architecture allows processing,

networking, and storage services to dynamically

Figure 5: Envisioned architecture for the Co-Fog technological platform of the distributed collaboration, Fc-A1, Fc-A2 and

Fc-A3 (resp., Fc-B1 and Fc-B2) clones; CNT=Container; F2U= Fog-to-User; U2F= User-to-Fog; F2F= Fog-to-Fog.

transfer the data at the fog node, cloud, and

users continuum. However, the interfaces for the

fog to interact with the cloud, other fogs, and

users, must facilitate the flexibility and dynamic

relocation of the computing, storage, and control

functions among these different entities. This will

enable well-situated end-user assessment for Fog

computing services and will also allow capable and

compulsory to support fog-to-cloud and vice-versa

collaboration which provides back-to-back

services. Fog-to-cloud interface also supports

functionalities, such as: i) functions at fog to be

supervised/managed with the Cloud computing

ability; ii) cloud and fog which transfer data to

each other for processing and comparing; iii)

cloud which can decide to distribute/schedule fog

nodes for allocation of the services on demand; iv)

cloud and fog mutually can differentiate for better

management computing services with each other,

and; v) cloud which can make the availability of

its services through fog-to-users. It is essential to

find out which information and services should be

transversely passed at fog and cloud. Regularity

and granularity of such data and information should

decide how fog or cloud can respond to that data.

share their data storage, computing, and processing

capability tasks with prioritized node functionality

system for one or several users. Multiple fog nodes

might also act together with service for backups of

each other.

In the figure 5, we can see the inter-Fog backbone

that provides inter-Fog connectivity and makes

feasible inter-Fog resource pooling. Inter-Fog

allows the hosted clones to exchange data by

establishing C2C collaboration over an inter-clone

transport connections and a set of inter-connected

fog nodes (e.g., Fog Node A and Fog Node B), that

act as virtualized cluster headers. These virtualized

cluster headers host the clones (e.g., Fc A and Fc B)

of the involved physical devices (e.g., Users (A1,

A2 and A3) and Users (B1 and B2)).

The clones exploit the Fog support, in order to

augment the computing-communication capabilities

of the associated devices (e.g., Users (A1, A2 and

A3) and Users (B1 and B2)). A commodity

User-to-Clone: User-to-Clone connections

corresponding clones by exploiting the support of

single-hop wireless access links.

The service models supported by the Co-Fog

platform with the following two main remarks.

First, since the fog nodes of figure 5 may play the

two-fold role of offloading and aggregating points

for the traffic generated by the underlying users,

the Co-Fog paradigm is capable to support, by

design, all the Up/Down Offloading, Aggregation

and Peer-to-Peer (P2P) service models. Second, we