Hadoop based enhanced cloud architecture for bioinformatic algorithms.
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
Explosion of biological data due to large-scale genomic research and advances in high throughput
data generation tools result in massive distributed datasets. Analysis of such large non-relational, het-
erogeneous, and distributed datasets is emerging challenge in data driven biomedical industries. Highly
complex biological data require unconventional computational approaches and knowledge-based solu-
tions. Distributed datasets need to be reduced to smaller datasets that can be eciently queried. Since
genomic and biological data is generated in large volume and is stored in geographically diverse loca-
tions, distributed computing on multiple clusters, our objective here is to assess the feasibility of using
Cloud based platform to analyze genomic big data. In this paper we present an enhanced Hadoop archi-
tecture to reduce computation by utilizing “Common Features” before performing redundant computa-
tion. The enhanced Hadoop architecture allows the jobs to share these common features among them.
The common features describe the contents of data in blocks and can be used to determine DataNodes
that store the required data.
9. 1934-2018 - IEEE Standard for Adoption of OpenFog Reference Architecture for Fog
Computing.
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
OpenFog Consortium--OpenFog Reference Architecture for Fog Computing is adopted by this
standard. OpenFog Reference Architecture [OPFRA001.020817] is a structural and functional pre-
scription of an open, interoperable, horizontal system architecture for distributing computing, storage,
control and networking functions closer to the users along a cloud-to-thing continuum of communi-
cating, computing, sensing and actuating entities. It encompasses various approaches to disperse Infor-
mation Technology (IT), Communication Technology (CT) and Operational Technology (OT) Services
through information messaging infrastructure as well as legacy and emerging multi-access networking
technologies
SUMMARY
Architectural patterns are great way to design an IoT deployment. They help tremendously to
solve commonly occurring problems. With the help of them, we can design scalable and reliable
design that helps in smooth IoT deployment and management. There are many architectural
designs that oers various features.
In this chapter, we have learnt the following:
• Various architectural patterns,
• How architectural patterns can be applied to solve various IoT problems,
• Four-layer architecture,
• Seven-layer architecture,
• Fog computing and its advantages,
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• Hadoop and its challenges,
• Hadoop Pattern 1 (Real-Time IoT Events Streaming),
• Hadoop Pattern 2 (Batch-Oriented Data Transfer),
• OpenStack Cloud Architecture,
• Cloud Topologies like dedicated cloud, dedicated private cloud, managed private
cloud, and hybrid cloud, and
• Role of Cloud in IoT.
In the next chapter, we will spend time understanding core IoT modules. Further we will
learn various protocols such as Infrastructure, communication/transportation, discovery, and
data protocol. We will explore several sensors such as temperature, pressure, humidity, and
water. We will also try to understand dierent endpoints and endpoint models. We will learn
various data communication design frameworks such as device-to-device, and device-to-cloud.
And finally learn about IoT data management.
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