As the IoT/IIoT quickly expands into the Internet of Everything, new demands are
placed on the existing cloud-based model. With today’s centralized cloud, concerns
arise as billions of connected devices flood both private and public networks with
seemingly an infinite amount of raw data. The industry is quickly realizing that the
cloud-centric model must evolve to meet the growing needs of businesses.
by Warren Kurisu, Director of product management. Mentor Graphics
What is the answer? Fog computing, a decentralized architectural
model that pushes the intelligence of data processing
out of the cloud and brings compute resources and application
services closer to the ground, or to the edge of the IoT network.
Fog computing addresses the demand for more high-speed
processing and analytics and improves overall network/system
responsiveness. The fog also addresses security issues, a major
concern within the IoT.
This article discusses the IoT market landscape and the fog infrastructure,
and how the motive, means, and opportunity exist
today for businesses to make a strategic shift to Fog Computing.
Also discussed is a technical architecture for a fog implementation,
as well as the requirements for a smart device to successfully
participate in the fog strategy.
A Rapidly Changing Landscape
It wasn’t too many years ago that the terms “cloud” and “IoT”
started to permeate our vocabulary. It’s amazing how quickly
we are now facing the challenge of evolving our infrastructure
to handle the Internet of Everything. IHS forecasts that the
installed base of connected things will grow from 15.4 billion
devices in 2015, to 30.7 billion in 2020, and 75.4 billion in 2025.
For perspective, it’s interesting to compare that number to the
projected world population (Figure 1).
Although these statistics account for everything that is connected,
including mobile phones and computers, the massive
growth will come from other “things” such as connected cars,
smart homes, smart grids, wearables, industrial equipment,
medical equipment, and anything else that can be connected
and collect data.
But, it’s not just about the devices – it’s also about the data.
According to Cisco Systems, the amount of data created by these
devices in 2015 was 145 zettabytes (ZB), and will reach 600 ZB by
2020. (Note: a zettabyte is one billion terabytes or 1021 bytes.)
With regards to IoT and cloud, at least one part of the
problem is very obvious. If all of the data storage, processing,
and analytics were to be cloud-based, the sheer amount of data
that would need to be transmitted would choke the networks.
There are other problems as well. If all decision making were to
happen in the cloud, the latencies would be too high for any real-time
decision making; how many network hops exist between
your device and cloud? Also, the costs of transport could also
be an issue; how many dedicated connections would a business
require to ensure connectivity? Finally, reliability and security
are also concerns; how do I ensure fast failover and how do I
protect critical information?
Fog Computing: A Working Definition
Before discussion continues further, it might be useful to
define the term Fog Computing. As you might imagine there
are many and varying descriptions. One very succinct definition
comes from a recent IEEE publication entitled “Fog Computing:
Helping the Internet of Things Realize its Potential.” In this
article, the author describes Fog computing as “A distributed
paradigm that provides cloud-like services to the network
edge. It leverages cloud and edge resources along with its own
infrastructure. In essence, the technology deals with IoT data
locally by utilizing clients or edge devices near users to carry
out a substantial amount of storage, communication, control,
configuration, and management. The approach benefits from
edge devices’ close proximity to sensors, while leveraging the on
demand scalability of cloud resources” (Figure 2).
A Fog Computing approach provides the
following benefits:
data analytics and system responses can be executed
within appropriate time constraints.
network load.
designed for fast failover.
appropriate place, and only critical information from the fog
would need to be sent to the cloud.
Is the Embedded Industry at an
Inflection Point?
Is the industry at an inflection point – with all the billions
of devices and zettabytes of data swirling about? At the recent
ARC Industry Forum in Orlando, Florida, where visionaries
and leaders from the world’s leading industrial companies came
together to discuss current and future trends and issues, a few
key takeaways emerged:
cloud strategies. They range from startups who have built
brand new infrastructure, to those who are still trying to
figure out how to get data from their brownfield devices.
desire to gather even more to progress the field of advanced
analytics.
about how to implement security in this world of connected
devices.
Overall, it appears the table is now set for advancement.
Companies have the motive, means, and opportunity to advance
their cloud-based architectures.
Motive: For competitive reasons, businesses are now defining
new business models that change the rules of the game. This
could include 3D printing a car in a local factory, or converting a
business from selling a product to that of selling a service.
Means: By leveraging high-performance connectivity,
increasing compute power, and implementing security technologies,
businesses can integrate powerful new devices into their
factories or into their systems to effectuate these new business
models.
Opportunity: As the business and technology landscapes
rapidly evolve and infrastructures are being upgraded, businesses
can implement strategies to take full advantage of the
capabilities and standardization that enable the IoT and Fog
Computing.
Fog Computing: Key Requirements
The overall Fog Computing architecture is feature-rich. After
all, the concept of Fog Computing is to bring cloud-like services
to the network edge. Figure 3 illustrates what a fog architecture
includes.
The various layers of the fog architecture ensure that data
storage, processing, and analysis occur at the most appropriate
place in the infrastructure, to ensure that requirements are satisfied
relating to bandwidth, latency, reliability, and scale.
How Mentor Enables Fog Computing
At Mentor Graphics our strength and depth of experience lies
within the lower two layers of the visual seen in Figure 3. These
layers are enabled by Mentor’s industry-leading embedded portfolio,
designed and developed to enable world-class edge devices
and gateways.
From the perspective of these two lower layers; Mentor meets
critical key requirements by providing software tools and runtime
environments that are:
devices with basic processing capabilities and connectivity
to more fully-featured Linux-based devices and gateways,
each with the ability to scale data storage and processing as
required by the fog architecture. Today’s designs are now
consolidating edge functionality on complex, heterogeneous
System on Chip (SoC) architectures, with a mix of real-time
operating systems (RTOS) and Linux capabilities.
west” to the network of connected devices and “north” to the
higher layers in the system, and directly to the cloud. Ethernet
and wireless (Wi-Fi, Bluetooth, etc.) are a must, along
with support for industry-specific protocols such EtherCAT,
OPC-UA, and Data Distribution Service (DDS). Cloud protocols
including HTTP, MQTT, and CoAP are also required.
er-on, authenticating every bit of code that gets subsequently
loaded and executed on the system. This enables security
of data at rest, data in use, and data in motion. Securing a
system in this manner provides assurances that the data in
the fog architecture can be trusted.
It’s all About the Smart Device
It’s been noted that the Industrial IoT (IIoT) begins at the
smart device level. These devices, which must be scalable,
connected, and secure, are the basis on which cloud and fog
architectures are built.
One such example, demonstrated by Mentor at last year’s
ARM Technology Conference, is a distributed medical application.
The demo consisted of a patient monitor, which aggregated
and processed data from a distributed set of sensors collecting
patient electrocardiogram (ECG), blood pressure, and pulse
information (Figure 4). The data communication was enabled
by Real Time Innovations’ Connext DDS integrated with both
Mentor Embedded Linux and Nucleus RTOS. This distributed
data can be captured, stored, and analyzed locally, and used to
generate real-time patient alarms and events. Critical patient
information could then be sent up to the cloud for remote monitoring,
clinic access, or advanced analytics.
Conclusion
As the number of connected devices and data grows exponentially;
solutions are required to ensure that data storage, data
transmission, analytics and system response is optimized from
the edge device to the cloud. We are now in a transition where
businesses are moving from a planning phase to implementing
their cloud strategies where Fog Computing is quickly gaining
favor among various industries and businesses.
Mentor Embedded has spent decades building an industry-leading portfolio which can be leveraged to build smart
devices that enable a cloud and fog strategy, and address some of
the issues that today’s businesses are facing.
Author Bio:
Warren Kurisu is the director of product management in the
Mentor Graphics Embedded Systems Division, overseeing the
embedded runtime platform business for the Nucleus RTOS, Mentor
Embedded Linux, virtualization and multicore technologies,
safety certified runtimes, graphics and development tools. Warren
has spent nearly 30 years in the embedded industry, both as an
embedded developer and as a business executive, working broadly
in industries including aerospace, networking, industrial, medical,
automotive, and consumer. Warren holds a master’s degree in
Electrical Engineering from the University of Southern California
and a Master of Business Administration from the University of
California at Berkeley.
