UNCOVERING THE LAYERS
highly distributed nature of modern digital
business applications.
The key challenges that the combination
of Edge Computing and hybrid multi-cloud
adoption will solve include:
• Lower latency and bandwidth
savings: Proximate high-speed,
low-latency connections (<60–<20
milliseconds) are necessary for
companies to materially close the
‘distance gap’ between their application
and data workloads and cloud service
providers (CSPs). With agile and
scalable cloud environments closer
to the users at the Edge, data access
and application response times can be
faster and cost savings from reduced
data transport can be realised.
• Enterprise consumption of hybrid
multi-cloud: Enterprises generally
determine which cloud platform to
place their applications on by which
CSP delivers the best service for a
specific workload. This freedom of
choice makes it easy and practical for
IT organisations to experiment with
different cloud platforms to see which
delivers the best quality of service
(QoS) at the best price. Additionally,
www.intelligentdatacentres.com
more than ever before, enterprises
require the flexibility of retaining
control and securely running business-
critical applications in-house and want
the flexibility of leveraging both private
and public hybrid cloud environments,
depending on specific use cases.
• Political and regulatory factors:
With more frequent and complex
incidents of security and privacy
breaches, many countries are
regulating where and how data can
be used. These privacy and data
sovereignty compliance requirements
will lead to more distributed data
centres and cloud services that keep
data local to a specific geographic
region or country. An aeroplane with thousands of
equipment sensors, an autonomous
vehicle producing telematics data, or a
smart hospital monitoring patients’ well-
being can each generate several terabytes
of data a day. About 75% of enterprise AI/
analytics applications will use 10 external
data sources on average.
AI and IoT will drive new
interconnection and data
processing requirements at
the Edge Yet Equinix believes for many use cases,
an additional set of stringent requirements
related to latency, performance, privacy
and security will require that some of the
AI/ML data and processing (both inference
and model training) be proximate to data
creation and consumption sources.
Equinix predicts that enterprises will
accelerate the adoption of AI and
Machine Learning (ML) for a broader
set of use cases, requiring increasingly
complex and more real-time-sensitive
processing of large data sets originating
from multiple sources.
To meet the scale and agility
requirements of the above, Equinix
believes businesses will continue to
leverage public cloud service providers,
while most will likely find ways to use an
optimal set of AI/ML capabilities from
multiple CSPs-effectively deploying a
distributed, hybrid architecture for their
AI/ML data processing.
Equinix predicts this will create an
impetus towards new architectures and
the increased adoption of vendor-neutral,
richly interconnected, multi-cloud-
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