K
Kathleen Martin
Guest
The world is entering the age of hyperconnectivity, where devices, data and information systems talk constantly, sharing data between and among numerous applications programmed to do everything from safeguarding our homes to running oil rigs.
This world of hyperconnectivity is awash in data.
IDC's Global DataSphere Forecast, 2021-2025 predicted that global data creation and replication will grow from 64.2 zettabytes of data in 2020 to 181 zettabytes in 2025.
More and more of that data creation will happen at the very end of computing networks, thanks to the rapid growth of IoT and its collection of connected endpoint devices.
And the amount of data that's processed at the edge of those computing networks is expected to grow just as rapidly.
Figures from Gartner, a tech research and advisory firm, confirm this dramatic shift. The firm found that approximately 10% of enterprise-generated data was created and processed outside of traditional centralized data centers and the cloud in 2018. But it predicted that 75% of data will be processed at the edge by 2025.
What is edge computing?
Edge computing is -- as the name so succinctly says -- is computer power that exists on the edge of a connected ecosystem. It's positioned physically close to the endpoint devices, such as sensors or mobile phones, that are generating the data.
The role of edge computing is to ingest data generated from the nearby endpoint devices and then use a machine learning program to first analyze that data and then direct an action in response to that analysis.
Edge computing is an alternative to sending endpoint-generated data to centralized servers -- whether on premises or, more likely, in the cloud -- for processing.
This edge computing capability is commonly housed in purpose-built devices, such as IoT gateways, but it can sometimes be housed in the endpoints themselves.
Benefits of edge computing
True to its name, edge computing takes compute out of an enterprise's core data center and places it close to endpoint devices where data is being generated, which brings several key benefits, such as:
1. Improved speed/reduced latency
By its definition and design, edge computing eliminates the need to move data from endpoints to the cloud and back again. Decreasing that travel shaves time off the entire process; this time savings can be measured in seconds, sometimes even milliseconds. That might not seem like much, but travel time -- known as latency -- is a critical consideration in a connected world where real-time decision-making capabilities are necessary for proper functioning of the endpoint devices.
For example, autonomous vehicles, industrial and manufacturing IoT deployments and medical use cases all require machines to analyze data and return instructions nearly instantaneously in order to function safely.
Continue reading: https://internetofthingsagenda.techtarget.com/tip/Top-5-benefits-of-edge-computing-for-businesses
This world of hyperconnectivity is awash in data.
IDC's Global DataSphere Forecast, 2021-2025 predicted that global data creation and replication will grow from 64.2 zettabytes of data in 2020 to 181 zettabytes in 2025.
More and more of that data creation will happen at the very end of computing networks, thanks to the rapid growth of IoT and its collection of connected endpoint devices.
And the amount of data that's processed at the edge of those computing networks is expected to grow just as rapidly.
Figures from Gartner, a tech research and advisory firm, confirm this dramatic shift. The firm found that approximately 10% of enterprise-generated data was created and processed outside of traditional centralized data centers and the cloud in 2018. But it predicted that 75% of data will be processed at the edge by 2025.
What is edge computing?
Edge computing is -- as the name so succinctly says -- is computer power that exists on the edge of a connected ecosystem. It's positioned physically close to the endpoint devices, such as sensors or mobile phones, that are generating the data.
The role of edge computing is to ingest data generated from the nearby endpoint devices and then use a machine learning program to first analyze that data and then direct an action in response to that analysis.
Edge computing is an alternative to sending endpoint-generated data to centralized servers -- whether on premises or, more likely, in the cloud -- for processing.
This edge computing capability is commonly housed in purpose-built devices, such as IoT gateways, but it can sometimes be housed in the endpoints themselves.
Benefits of edge computing
True to its name, edge computing takes compute out of an enterprise's core data center and places it close to endpoint devices where data is being generated, which brings several key benefits, such as:
1. Improved speed/reduced latency
By its definition and design, edge computing eliminates the need to move data from endpoints to the cloud and back again. Decreasing that travel shaves time off the entire process; this time savings can be measured in seconds, sometimes even milliseconds. That might not seem like much, but travel time -- known as latency -- is a critical consideration in a connected world where real-time decision-making capabilities are necessary for proper functioning of the endpoint devices.
For example, autonomous vehicles, industrial and manufacturing IoT deployments and medical use cases all require machines to analyze data and return instructions nearly instantaneously in order to function safely.
Continue reading: https://internetofthingsagenda.techtarget.com/tip/Top-5-benefits-of-edge-computing-for-businesses
