Industry executives and experts share their predictions for 2020. Read them in this 12th annual VMblog.com series exclusive.
By Sastry Malladi, CTO, FogHorn
Edge Computing in 2020 - What is it, where is it, and why is it important?
Over the past year, many industry players increased their focus on
edge-based solutions, and organizations are now beginning to understand the
value true edge computing can bring to their Internet of Things (IoT) and
Industrial Internet of Things (IIoT) projects. For example, for automotive and
transport-focused use cases, edge computing helps maximize the efficiency and
lifespan of battery and charging systems as well as other systems that support
braking, motor performance, safety, passenger environment, and predictive
maintenance. Moreover, edge solutions enable high-performance processing
directly inside fleet vehicles for critical command and control decisions and
to minimize the cost and latency associated with uploading huge amounts of data
to remote data centers.
In 2020, as more IoT projects move from proof-of-concept to full
deployments, keep an eye on these emerging industry trends:
The
industry will refine the definition of "edge"
This year
many industry players led conversations regarding the exact definition and
various locations of the edge. Organizations have struggled to understand
the precise location of the edge when, in reality, the location is highly
dynamic and varies by industry and use case. For example, telecom operators
consider the edge of the telecom network the true edge (also called the service
edge), whereas application developers and industrial plant operators define it
as the point of data production (or the location of the asset being monitored).
The telco definition of the edge also aligns with MEC (Multi-access Edge
Computing).
Moreover,
some solutions adopted edge terminology without considering its exact
characteristics, thus introducing more confusion to the market. Weak (or fake)
edge solutions lack the ability to optimally run analytics and machine learning
models on the live streaming data in a constrained compute environment, a
crucial requirement for deriving actionable insights in real-time. These
solutions are not ‘true edge' as they rely on the cloud for data processing,
rather than processing data at the edge.
Lastly,
confusion regarding the edge-cloud relationship. Edge is certainly
complementary to cloud, although in the industrial sector, edge greatly
enhances the cloud adoption and value. Indeed, over the next year, edge
computing leaders will continuously work to evolve and refine answers to
questions such as: where is the edge located, what is edge
computing; and why is the edge important.
Automotive
manufacturers will look to edge computing to improve real-time functionalities
and accelerate autonomous operations
Cars
generate significantly more data today than ever before, and it is a big
challenge to gather, merge, process, and deploy all that sensor data
efficiently. The future of transportation with autonomous vehicles (AV) depends
on creating the required intelligence and processing to build and operate
sophisticated, autonomous systems. For example, many AVs are expected to be
electric cars, and these will require substantially more in-vehicle
intelligence and system life cycle management. These are needed to maximize the
efficiency and lifespan of battery and charging systems, as well as other
systems supporting braking, motor performance, safety, passenger environment,
and predictive maintenance.
While
fully autonomous vehicle controls are years away, there are many existing edge
computing applications now available to enhance the efficiency, reliability,
and safety of commercial and public transportation. These include vehicle
control and safety systems, such as cameras, driver assistance, and collision
avoidance functions, that are being added to new vehicles every year.
In the
year ahead, rather than relying on remote data centers for critical command and
control decisions, automotive manufacturers can eliminate safety concerns and
fast-track the road to autonomous driving by deploying edge-enabled systems.
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About the Author
As CTO of FogHorn, Sastry is responsible for and oversees
all technology and product development. Sastry is a results driven technology
executive with deep technology and management experience of over two and half
decades. His areas of expertise include developing, leading and architecting
various highly scalable and distributed systems, in the areas of Big Data, SOA,
Micro Services Architecture, Application Servers, Java/J2EE/Web Services
middleware, and cloud Computing to name a few.
Prior to joining FogHorn as CTO, Sastry was Chief
Architect of StubHub, an eBay company where he led the technology architecture
transformation and also spearheaded the Big Data initiatives and data driven
decisions. Sastry was also a key technology executive at eBay that lead the
technology re-platforming effort from its monolithic architecture to the
distributed, and scalable service oriented architecture that it is today
enabling the business growth.