Finding Your Edge Is Easier Than Ever
As the edge of your network proliferates and becomes more sophisticated and mission critical, the haphazard approach to site configuration becomes inefficient, expensive, and ultimately, unsustainable. No two edge sites are exactly alike, but they often share key characteristics that establish parameters for important decisions around equipment selection and architecture.
This tool looks at the characteristics of your edge — everything from location and external environment to number of racks and the power they require — and identifies the appropriate edge infrastructure model for that site. It allows you to make smarter, faster decisions and quickly deploy edge equipment that optimizes site efficiency while reducing costs and service demands. To get started, simply tell us about your edge.
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Or select which use case you would like to explore:
Manufacturing
Retail
Healthcare
Smart City
Education
Telecoms
Data intensive
Advanced predictive maintenance
Smart
Security
Real-time inventory management
HD Content Distribution
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Human latency sensitive
AR/VR
Natural language processing
M2M latency sensitive
Real-time precision
monitoring
Smart grid
Select an industry to filter the use cases:
Smart microgrids respond to real-time changes in supply and demand to make decisions such as whether power is also needed from the centralised grid or what form of energy (renewable or not) is needed at what time.
Drone technology has progressed from basic remote control movement, to having (HD / 4K) video, photos, high value instrumentation, intelligent piloting and autonomous flying modes. As the technology has progressed and become more intelligent, the amount of operational and collected data is increasing exponentially.
Predictive maintenance monitors data from sensors on equipment to ensure the equipment is in good condition and flag pre-emptively if there is a need to repair it, eliminating the need for scheduled maintenance.
The use of cameras, sensors, IoT, and data analysis technology for better security, surveillance, and monitoring.
Collection of large amounts of data on the
real-time position and status of goods to gain awareness of what stock is where in the supply chain at any given time, at the granularity of the individual product level.
Provision of low latency, high definition content. This is to serve consumers who want ever increasing levels of definition and quality related to the content (videos, images, etc.) they consume. The doubling of definition every couple of years typically requires twice the amount of data to be transferred.
Connected cars gather and share vast amounts of data related to physical location, local environmental conditions, and details on the car and how it is operated. They can also consider the entertainment needs of the passengers. Fully autonomous cars are able to move independently without any human input or monitoring.
Connected monitoring devices (e.g. glucose monitors, health tools and other sensors) enable right-time notifications to practitioners of unusual trends (through analytics / AI). This enables creation of patient dashboards that offer full visibility, and relevant data can be stored securely in a cloud system.
Autonomous robots are intelligent machines capable of performing value add tasks in the real world with a high degree of independence. Examples include replacement of Service and Manufacturing jobs.
The processing of natural langauge (e.g. speech and text) by software. This enables interaction with everyday IT applications by voice, or the analysis of large sets of unstructured text or speech data (e.g. to synthesize patient notes in healthcare).
Wearable devices generate lifelike images, sounds and other sensory stimulation, or superimpose computer generated images onto a user's view of the real world.
Enablement of real-time monitoring of machines and processes as well as product quality through advanced analytics. This data can then be fed back in order to optimize efficiency and minimize defects.
Data intensive
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The use of cameras, sensors, IoT, and data analysis technology for better security, surveillance, and monitoring.
Collection of large amounts of data on the
real-time position and status of goods to gain awareness of what stock is where in the supply chain at any given time, at the granularity of the individual product level.
Provision of low latency, high definition content.
This is to serve consumers who want ever increasing levels of definition and quality related to the content (videos, images, etc.) they consume. The doubling of definition every couple of years typically requires twice the amount of data to be transferred.
Connected cars gather and share vast amounts
of data related to physical location, local environmental conditions, and details on the car and how it is operated. They can also consider the entertainment needs of the passengers. Fully autonomous cars are able to move independently without any human input or monitoring.
Drone technology has progressed from basic remote control movement, to having (HD / 4K) video, photos, high value instrumentation, intelligent piloting and autonomous flying modes. As the technology has progressed and become more intelligent, the amount of operational and collected data is increasing exponentially.
Connected monitoring devices (e.g. glucose monitors, health tools and other sensors) enable right-time notifications to practitioners of unusual trends (through analytics / AI). This enables creation of patient dashboards that offer full visibility, and relevant data can be stored securely in a cloud system.
Autonomous robots are intelligent machines capable of performing value add tasks in the real world with a high degree of independence. Examples include replacement of Service and Manufacturing jobs.
The processing of natural langauge (e.g. speech and text) by software. This enables interaction with everyday IT applications by voice, or the analysis of large sets of unstructured text or speech data (e.g. to synthesize patient notes in healthcare).
Wearable devices generate lifelike images, sounds and other sensory stimulation, or superimpose computer generated images onto a user's view of the real world.
Enablement of real-time monitoring of machines and processes as well as product quality through advanced analytics. This data can then be fed back in order to optimize efficiency and minimize defects.
Smart microgrids respond to real-time changes in supply and demand to make decisions such as whether power is also needed from the centralised grid or what form of energy (renewable or not) is needed at what time.
Select which use case you would like to explore
M2M latency sensitive
Smart grid
Real-time precision
monitoring
Human latency sensitive
AR/VR
Natural language processing
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Data intensive
HD Content Distribution
Real-time inventory management
Smart
Security
Advanced predictive maintenance
Manufacturing
RESET FILTERS
M2M latency sensitive
Smart grid
Real-time precision
monitoring
Human latency sensitive
AR/VR
Natural language processing
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Data intensive
HD Content Distribution
Real-time inventory management
Smart
Security
Advanced predictive maintenance
RESET FILTERS
Retail
M2M latency sensitive
Smart grid
Real-time precision
monitoring
Human latency sensitive
AR/VR
Natural language processing
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Data intensive
HD Content Distribution
Real-time inventory management
Smart
Security
Advanced predictive maintenance
RESET FILTERS
Healthcare
M2M latency sensitive
Smart grid
Real-time precision
monitoring
Human latency sensitive
AR/VR
Natural language processing
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Data intensive
HD Content Distribution
Real-time inventory management
Smart
Security
Advanced predictive maintenance
RESET FILTERS
Smart City
M2M latency sensitive
Smart grid
Real-time precision
monitoring
Human latency sensitive
AR/VR
Natural language processing
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Data intensive
HD Content Distribution
Real-time inventory management
Smart
Security
Advanced predictive maintenance
RESET FILTERS
Education
M2M latency sensitive
Smart grid
Real-time precision
monitoring
Human latency sensitive
AR/VR
Natural language processing
Life Critical
Autonomous Robots
In-hospital patient monitoring
Smart
drones
Connected & autonomous cars
Data intensive
HD Content Distribution
Real-time inventory management
Smart
Security
Advanced predictive maintenance
RESET FILTERS
Telecoms
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As the edge of your network proliferates and becomes more sophisticated and mission critical, the haphazard approach to site configuration becomes inefficient, expensive, and ultimately, unsustainable. No two edge sites are exactly alike, but they often share key characteristics that establish parameters for important decisions around equipment selection and architecture.
