Introduction to Edge Computing
Processing data near the edge of the network, where the data is generated, instead of a centralized data-processing warehouse. Edge computing enables mobile computing and IoT technologies. It makes data and devices more affordable and connected without increasing responsiveness and reducing latency. Let’s take a deep dive into edge computing and understand how the technology works with Akira.ai's solution for business users.
Why we need Edge Computing?
With the emerging technologies like deep learning and neural networks which have revolutionary potential totally depending on cloud computing hampers its runtimes increasing massive power requirements.
Forrester Research an International Informational Technology Firm reports that “ Latency is becoming an issue as firms try to push more data to software that runs in the cloud or in the data center”. As the amount of data is increasing it is becoming more uneconomical to do all processing centrally.
Edge Computing acts as efficient technology that brings intelligence closer to the place where intelligence is needed and in return unleash the collective power of intelligent devices. As the number of firms are growing and increasing central software platforms handling inflow of data are being pushed to the edge. Main motivations that let’s Akira.ai choose Edge Computing are -
- Real-time data processing without latency or delay in the transfer of data.
- Eliminates lag time or allows smart applications to respond to data instant.
- A large amount of data processed near sources resulting in reduced internet bandwidth.
- Eliminates costs ensuring applications to be used in remote locations.
- Processing data without putting in the cloud adds security for sensitive data.
Tools of Edge Computing
Gateway Servers - It serves as the connection point between edge device or edge server and cloud.
Cloudlets – Cloudlet is a small-scale data center or cloud located at the edge of the internet. Typically used for mobile consumers, connects from one cloudlet to another and brings cloud-computing capabilities closer to the consumer.
Fog nodes – Fog layer consists of storage and network devices like controllers, switches, routers, and servers. These devices are called fog nodes.
Microdata Centers – These are small scale data centers hosted in cloudlets. It is not a requirement but rather an enhancement to Fog Computing. It breaks the cost of centralized data centers but converting it into multiple micro data centers.
Edge Computing Use Cases in general
Field & Industrial IoT - Field and Industrial IoT uses IoT technologies to enhance manufacturing and industrial processes. The edge computing has become critical for the manufacturing and industrial processes to process large amounts of data with rapid action and instant repeated reaction times.
Smart Cities & Architecture - To meet smart cities objective and architecture such as parking meter, connected trash bins and sensors throughout metropolitan areas where data is gathered Industrial IoT devices and edge computing works together realize, address and optimize these requirements.
Customer Experience in Retail & Hospitality - The data collected and analyzed to record customer sentiment and social media activity helps to improve customer experience. The data is captured on terminal or Point of Sale (POS).
Connected Vehicles - The IoT device specifically autonomous vehicle is a great example for this use case. Telematics data for auto insurance is another example that influences dynamic pricing. Edge computing makes interface data well with constant communication with multiple networks.
Facial and Image Recognition - With machine learning & deep learning getting better the use of facial & image recognition continue to expand. From cloud in past now it is all about edge devices bringing connectivity, computing and content capabilities closer to you with edge compute.