What Is Edge Data Centers?
Edge data centers are smaller facilities located close
to the populations they serve that deliver cloud computing resources and cached
content to end users. They typically connect to a larger central data center or
multiple data centers. By processing data and services as close to the end user
as possible, edge computing allows organizations to reduce latency and improve
the customer experience.
Why Do We Need Edge Data Centers?
Latency has always been a problem for data center managers,
but in recent years, it’s become a critical concern due to Big Data, the Internet of Things, Cloud and streaming services, and other technology trends.
End users and devices demand anywhere, anytime access to applications,
services, and data housed in today’s data centers, and latency is no longer
tolerable. As a result, organizations across many industries are establishing
edge data centers as a high-performance and cost-effective way to provide
customers with content and functionality.
Key Characteristics
of Edge Data Centers
Edge data centers may be defined differently by different
data center professionals based on their roles, industries, or priorities, and
due to the relative infancy of edge data centers as an established trend.
However, most definitions share the following key characteristics:
Local: Edge data centers are placed near the areas they serve and are managed remotely.
Small: Edge data centers have the same components of a traditional data center but packed
into a much smaller footprint.
Part of a larger
deployment: An edge data center is one of many in a complex network
including a central enterprise data center.
Mission critical: Edge data centers house mission-critical data, applications, and services for
edge-based processing and storage.
Top Use Cases of Edge Computing
Currently, about 10% of data is created and processed
outside a centralized data center or Cloud, but by 2025, this figure will reach
75% (Gartner, 2018). Here are some of the top use cases for edge
computing.
Autonomous vehicles: Self-driving vehicles can collect, process, and share data in real time, making transportation safer.
Smart cities: Real time gathering and analysis of data on traffic, utilities, and infrastructure allows city officials to immediately respond to problems.
Manufacturing: Equipping industrial IoT devices with data storage and computing capabilities allows for better predictive maintenance and energy efficiency.
Financial institutions: With reduced latency for high-volume banking firms, trading algorithms are executed quicker, potentially making more profit.
Telemedicine: Healthcare providers can have immediate access to critical patient data collected from personal health monitoring devices and fitness bands.
Augmented reality: AR technology, which requires real time data processing, is being deployed by retail chains to create a more immersive in-store shopping experience.
AI virtual assistants: The processing burden of household virtual assistants is distributed locally for improved performance and reduced latency.
Video monitoring: Video cameras, especially those equipped with motion detection or facial tracking, record massive amounts of data that can be collected and processed locally.
Gaming: Multi-player gaming relies on high bandwidth, low latency, and local matchmaking, leading to the emergence of cloud gaming.
Content delivery: Content cached at the edge can be delivered to the end user
in a matter of single milliseconds.
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