Why Edge Computing is important?

What is edge computing, Examples of edge computing, benefits of edge computing, use cases of edge computing

What is Edge Computing? Data in the modern world providing valuable business insight on a day-to-day basis and supporting real-time control over high-risk processes and operations.

Where does all this data come from?

The data is created, by human beings, as we interact with our world, in the environments that we operate and in the places where we do work. But in today's world, we have a huge amount of data. This data can be collected from sensors, Bluetooth devices, and IoT devices that operate in real-time from remote areas almost anywhere in the world. But this flow of data is also changing the way that businesses are computing themselves. Content Delivery Network is an important edge and cloud concept. That brings the most accessed content close to the user, for faster access and better user experiences.

What is Edge Computing?

Edge computing is a distributed computing technology framework in which client application is processed by connected network devices. These devices such as local edge servers or IoT devices. In simplest terms, edge computing moves a part of resources out of the central system and closer to the source of data itself. Instead, send real-time data to a central system for processing, it is performed and processed where the data is generated. Computing takes place near the physical location of the source data. It is considered for end-user solutions.

So, edge computing is going to grow very fast. It will have as much of an impact, or even more, on the enterprise world as smartphones did for the world of consumer computing.

Some examples

1.    Predictive analytics can guide equipment maintenance and repair before the actual defects or failures occur.

2.    Water treatment or electricity generation to ensure the equipment is functioning properly and to maintain the quality of output.

As IoT and digitalization of business grow there are increasing challenges of managing large amounts of data. Today it's around 10% of the problem is that when being processed in the company's data centers and the cloud the network bandwidth reaches the limit. Producing data agglomeration, slow response time, and excessive costs.

Edge computing is bringing the compute power to wherever your data resides. So today a lot of big data implementations or initiatives have failed. Because you cannot aggregate and create a single repository for all the data that is being created within the organization. Rather than trying to force a huge data lake concept allow your data to remain distributed within the organization and bring the compute resources to that data. That would also be considered an edge computing strategy.

Some of the triggers that might get you to consider implementing an edge computing strategy.

Does your service need large volumes of digital interactions?

Edge computing is designed to handle large volumes to avoid the backhaul of large volumes of data to a central data processing center.

Is the end user's digital experience critical to your success?

The value proposition of your organization is a digital experience. Edge computing might be a significant advantage for you.

Is your digital interactions time-sensitive?

Customer experience benefits from reducing latency of response within the application or whatever service that are providing over this digital platform.

Do you have significant ingress or egress charges in your current cloud platform?

One of the benefits of edge computing platforms is that they are meant to handle large volumes. Thus the charges for data inflows and data outflows tend to be either non-existent or very cheap compared to some of the alternatives in other public cloud environments.

Are you exploring augmented reality/ virtual reality- even if it's way out in the distance?

Implementing edge computing can build those types of services on an edge computing platform. Rather than trying to implement edge computing while you are also trying to implement something as sophisticated as virtual or augmented reality.

Is your user base broadly distributed geographically?

The spread-out end-user base might be able to justify an edge computing strategy by again delivering those services into the markets. Specifically, customers are rather than backhauling all their experience, all their data into a centralized processing facility.

Are you or will you be subject to different consumer privacy issues?

Most of the states will adopt the consumer privacy act standards. So the consistent platform upon which people can architect their consumer privacy framework. But multinational organization and have data sovereignty issues as well as individual. Consumer privacy issues having an edge computing strategy might make those issues and the frameworks that need to follow within each of those markets much easier to follow terms.

Benefits of Edge computing

It is easier to deploy compute resources near the data than to move the data to the compute resources.

Improved response time for the end-user.

Reduce the data transmitted the amount of volume into a central compute location and this eases congestion.

Edge computing has a different resiliency strategy.

Edge Computing Use Cases

Autonomous driving

IoT sensor data processing. In a world that is increasingly IoT where remote sensors are sending information out into large databases. And that information needs to be processed, analyzed, and retained, or discarded based upon certain requirements. Edge computing can make a lot of sense in these situations.

Ex. Manufacturing hospitals, Real estate.

Warehouse operations becoming more automated. The data from those automated platforms need to be housed and analyzed.

An edge computing environment might be a very effective Secure Access Service Edge (SASE).

Machine learning and AI applications

A lot of organizations have yet to figure out how to use machine learning and artificial intelligence in their business operations. But some of the divisions within those organizations are using it today. The marketing organization may be instituting a marketing automation platform.

Data analytics and metadata extraction

All the raw data into your data center need to understand top-level information from that data.

Retail is an example for tracking customers within a store, messaging customers. While they are in that store environment or outside of the store environment.