Early Internet of Things(IoT) applications have been build around cloud-centric architectures where information generated at the edge by the “things” in conveyed and processed in a cloud infrastructure. These architectures centralise processing and decision on the data-centre assuming sufficient connectivity, bandwidth and latency.
As applications of the Internet of Things extend to industrial and more demanding consumer applications, the assumptions underlying cloud-centric architectures start to be violated as, — for several of these applications — connectivity, bandwidth and latency to the data-centre are a challenge.
Fog and Mist computing have emerged as forms of “Cloud Computing” closer to the “Edge” and to the “Things” that should alleviate the connectivity, bandwidth and latency challenges faced by Industrial and extremely demanding Consumer Internet of Things Applications.
This presentation, will (1) introduce Cloud, Fog and Mist Computing architectures for the Internet of Things, (2) motivate their need and explain their applicability with real-world use cases, and (3) introduce the concept of fluid IoT architectures and explain how these can be architected and built.
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The Cloudy, Foggy and Misty Internet of Things -- Toward Fluid IoT Architectures
1. The Cloudy, Foggy and Misty Internet of Things
Toward Fluid IoT Architectures
Angelo Corsaro, PhD
Chief Technology Officer
angelo.corsaro@prismtech.com
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The majority of IoT systems
are today cloud-centric
These systems are
characterised by device-to-
cloud communication and in-
cloud analytics
Cloud-Centric
Architectures
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Cloud-Centric
IoT Platforms
The large majority of IoT
platform have been built
with Cloud-Centric
architectures in mind
AWS IoT
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*IDC
*CISCO
CISCO estimates an average
of 6.6 devices per person
leading to 50B devices in
2020.
Too many Devices
IDC estimates 27.9 devices
per person leading to 212B
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Industrial IoT applications such
as Smart Grids , Smart
Factories, Smart Farming,
Connected Vehicles and Smart
Cities are not compatible with
the assumptions of Cloud
Centric Architectures
Industrial IoT
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There is sufficient
bandwidth to push data to
the Cloud.
Cloud-Centric
Architectures
Assumption #1
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Smart Factory
0.5 TB of data
produced per day
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Connectivity is not an
issue. A device will (almost)
always be connected to the
cloud.
Cloud-Centric
Architectures
Assumption #2
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Autonomous
Vehicles
coordination of fast moving
autonomous vehicles
intermittent
connectivity
dynamic pairing of devices
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The latency induced by
cloud-centralised analytics
and control is compatible
with the dynamic of the IoT
system
Cloud-Centric
Architectures
Assumption #3
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Latency
Constraints
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The connectivity cost is
negligible
Cloud-Centric
Architectures
Assumption #4
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Cost of connectivity is an
issue in Smart Grids as the
operator has to pay for the
2G/3G/4G data-link
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Industrial companies are
comfortable in exposing
their data to the cloud.
Cloud-Centric
Architectures
Assumption #5
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Fog-Centric
Architectures
fog
fog
fog
cloud
Fog Computing
Architectures extend
elastic compute,
networking and storage
across the cloud through
to the edge of the network
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Fog/MEC-Centric
IoT Platforms
IoT Platforms support for
Fog /MEC computing is
rapidly emerging
IOx
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OpenFog
Consortium
The recently established
OpenFog is accelerating
and facilitating the
expansion, convergence
and interoperability of Fog
computing infrastructures
[source: OpenFog Whitepaper http://bit.ly/openfog-wp]
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[most of] Fog centric
infrastructures rely on
edge servers to provide
elastic compute, store and
communicate abstractions.
Yet, are incapable of
exploiting resources
available on the Things
What about
the Things?
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Mist-Centric
Architectures
fog
fog
fog
cloud
mist mistmist
Mist Computing
Architectures extend elastic
compute, networking and
storage across the Fog
through to the Things
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Any use of these slides that does include me as Author/Co-Author is plagiarycloudy…foggy…misty…
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42
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maybe…
but that doesn’t help
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Mist
Fog
Cloud
Things
Fog
Nodes
Cloud
Servers
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Provisioning,
deployment &
Management
Application
Bundle
Container
VM
The unit of provisioning
and deployment
supported by Cloud, Fog
and Mist infrastructure
are different
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time
nownow-t
near past
past
data flow
Data in MovementData at Rest
Data Stores Data Streams
Data Access
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Memory hierarchy
Everyone gives for granted
that the memory
hierarchy present in
computing systems should
be transparent
Why shouldn't the same be
true for data access in IoT
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Analytics
Different analytics
technologies are
applicable for Cloud, Fog
and Mist Computing
As a result there is no
decoupling between the
algorithm and the
deployment!
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Map/Reduce
applicability
Most analytics framework
built for cloud are based on
Map/Reduce
Map/Reduce is applicable to
embarrassing parallel
computational problems.
These are a small subset of
analytics required in IoT!
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Architectural consistency
and composability is key
to scale
A unifying architectural
principle should be the
reference for IoT Platforms
Architectural
consistency
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FLUID IOT
Architecture
The Fluid IoT Architecture
eliminates the
technological segregation
created by Cloud, Fog and
Mist technologies and
abstracts compute, storage
and networking end-to-end
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FLUID IOT Platform
Allow users to express,
provision and manage
computations operating over
virtualised resources (e.g.
devices) and data
(potentially loads of these)
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Storage Compute Networking
Data Virtualisation
Analytics
Node Monitoring & Management
OS
HW
Node
virtualisation
Vertical
Services
Preventive Maintenance
…
Applications
Platform
virtualisation I/O
Security
Platform Management
Traffic Management
Smart City Management
Visualisation
Security
IOT Platform
The Fluid IoT
Platform virtualises
resources end-to-end
and unifies
abstractions
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FLUID IOT Platform
Fluid IoT Platform will provide
uniform abstractions to
provision, manage and
deploy applications
Fluid IoT will virtualise data
access and unify analytics
across Cloud, Fog and Mist
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Provisioning,
deployment &
Management
“Fluid”
Bundle
A Fluid IoT Platform
provides a uniform way of
provisioning, deploying and
managing bundles across
computational resources
that may be available on
Things, Edge Nodes of IaaS
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time
nownow-t
near past
past
data flow
Data in MovementData at Rest
Data Stores Data Streams
Data Access
Virtualised Data Streams
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Analytics
A Fluid IoT platform allows
to define Analysts
independently of their
deployment
This strongly leverages the
Data Virtualisation
provided by the platform
“Fluid”
Analytic