To understand edge computing, it helps to understand cloud computing. In cloud computing, device-generated data streams are routed to external networks and processors -potentially located in another state/province or abroad- for analysis. The analyzed information is then sent back to the device.
For example, a smart-home security camera sends its feed to a distant cloud-based network, which can identify whether or not someone has trespassed. Then, the information is sent back to the device.
Edge computing means that the processing and analysis of information takes place much more locally. Within the edge, work can be performed in the edge network, the edge server, a hybrid, or within the device itself.
This workload configuration has numerous advantages, from greater compute expediency and reduced latency to lower bandwidth requirements for transporting the data, key issues that have arisen through the expanded adoption of the cloud.
What are potential use-cases for edge computing?
Edge computing has limitless potential, but projects that excite business leaders and engineers alike include:
- Enabling industrial machines to more accurately assess and account for the causes of any seeming malfunctions. Local processing of data will mean that machines can detect whether they’re overheating due to the sun, or due to other causes. These type of self-conducted analyses can improve operational efficiency.
- Providing healthcare organizations with the capacity to process data in real-time. This could potentially improve patient experiences, and even affect health outcomes.
- Improving the speed and efficacy of real-time decision making within autonomous vehicles. By processing data locally, autonomous vehicles will be able to more adroitly adapt to rapidly changing traffic patterns and obstacles.
What is the difference between edge computing and fog?
The edge and fog are similar, as they both push data processing and analysis to locations that are close to the data’s origin. Edge computing typically occurs either occurs on the device’s sensors, or in a gateway that is on the device. Fog computing involves processing information on LAN hardware, making processing and analysis ever so slightly more distant from the device itself.
For more information on the distinction between the edge and fog, click here.
What are the key benefits of edge computing?
- Reduced processing latency.
- Fewer demands on cloud bandwidth.
- Decreased server resource requirements.
- Possible cost savings.
What are the challenges associated with edge computing?
- Processing power: Edge computing uses smaller processors and storage platforms than are available within the cloud. This means that users will experience greater limitations when it comes to throughput. Sending too much data into an edge infrastructure can overwhelm the system.
- Security: As with IoT, the design of edge computing took place first, and security surfaced as an afterthought.
How secure are edge computing devices?
The evolution of security mechanisms for edge computing has not kept pace with the evolution of the edge, itself. When deploying edge-based infrastructure, be sure to apply two factor authentication, and to manage your passwords. Additionally, edge computing isn’t just a cyber security risk. It also presents physical security risks. When data processing takes place in a local space or on a local device, a bad actor may choose to destroy the local infrastructure.
For more on this topic, be sure to regularly visit Cyber Talk.