Role of IoT Analytics in CDN
Media Entertainment Tech Outlook | Friday, April 09, 2021
Content delivery networks are at the forefront of edge computing in the age of the internet.
FREMONT, CA: Modern content delivery networks (CDNs) are unearthing new opportunities to pair endpoint-implemented uses with endpoint device content analysis. Rather than simply allowing clients to push their apps and data closer to the customer, these edge technologies are gathering data from internet-of-things devices and end-user mobile devices, then allowing near-real-time AI/machine learning of this data to help the application best understand what the customer is experiencing right now and how the app can offer the most needed and engaging insights.
There are several use cases where this value can be so empowering. Among these are apps for sending accurate data about public transportation that is offered upfront to get the customer to the flight or subway, once on board, to offer rich, targeted data and entertainment. Retailers, healthcare firms, and several others require empowering their app experiences with real-time insights within their facilities for personalized customer experiences. And gaming firms know that to ensure strong online social experiences, they require to verify who is available, has a network connection, and is ready to play.
CDNs were at the front of edge computing in the early days of the internet. The chart below shows the widening reach of CDNs over the past 30 years. With the present high volume of employees working from home due to mandates, edge content delivery and experience optimization are vital for the company's and/or government agency's success. These potentials help empower the employees and customers to have better experiences from home.
The leading cloud platforms and CDNs have widened their global market reach substantially by placing servers worldwide, and their edge computing provides reduced latency by allowing apps to be placed very close to the end-users. Cloud platforms have also capitalized heavily in edge computing and IoT data analysis. These serverless and function-as-a-service (FaaS) platforms are rapidly becoming implementable at the edge rather than just within the public cloud data centers.