The purpose of this article is to introduce the Edge Computing as a functional and non-functional extension to the IoT (Internet of Things) and the Cloud Computing service model. The Edge Computing is a powerful extension to the Cloud and the IoT based Big Data Analytics projects.
Let me set the context by giving you a brief background on the Cloud and the IoT relationship, then introduce the Edge Computing in this context. My aim is to explain the concept as simple as possible.
We all know the recent trends for Cloud Computing. The Cloud marked a paradigm shift to Information Technology and Computing field.
The IoT Cloud is a critical player in the data ecosystem of large business organisations. The central role the Cloud plays in the IoT is to facilitate data integration of the solution components effectively.
IoT solutions are mainly used to provide real-time information to consumers.
The data required to generate real-time information can be massive in scale.
The Cloud, along with computing power, storage, networking, analytics, metering, billing, and other service management components, can make this vast information available for the consumers.
The integration of the Cloud to the IoT can create new revenue streams for business organisations. Integrating the Cloud with the IoT can create new business models enriched by real-time analytics and directly consumed information at the same time.
In other words, the IoT is empowered by Cloud capabilities. Without the Cloud, the IoT can hardly add any value due to its real-time data and information-rich nature.
The addition of the Cloud to the IoT can also contribute to improved security, availability, scalability, and performance of the IoT solutions.
Cloud providers have rigorous security, availability, scalability, and performance metrics established based on a service consumption model. In particular, the IoT-enabled Cloud systems seem to pose additional security measures.
However, the next critical innovative integration to IoT Cloud is the Edge Computing.
After Cloud Computing, the Edge Computing can be seen as a paradigm shift in the IT industry.
What is Edge Computing?
The purpose of the Edge Computing is to bring computing and data storage components close to the service consumption locations.
Image ref: https://en.wikipedia.org/wiki/Edge_computing#/media/File:Edge_computing_infrastructure.png
This innovative approach can dramatically improve network bandwidth and network response times especially by reducing network latency.
The key technical functionality of the Edge Computing is to reduce the volumes of data and ease the data traffic in costly business networks.
This functionality can lower detrimental network latency and reduce data transmission cost.
The key use case for the Edge Computing is the computation offloading for real-time IoT applications.
The prime example of computation offloading use case is facial recognition data in airports and other secure public places.
The computation offloading is also widely used for computer games based on virtual reality, augmented reality, cognitive computing, and artificial intelligence features.
The Edge Computing gained significance acceptance from various industries and business applications.
For example, The Edge Computing is currently being used by smart cities projects, self-driving car systems, and home automation initiatives.
What is special about these applications and initiatives?
These applications and initiatives are data-hungry, in fact, they use streaming Big Data as their main input mechanism.
There are several requirements and use cases that these data sets can be injected from multiple remote sites.
Therefore, the Edge Computing solutions implemented in local sites can provide tremendous support to the initiatives by storing, analysing, filtering, and only sending the essential streaming data to the target IoT systems and the Cloud storage.
Architectural Importance of Edge Computing
When integrated with the Edge Computing in the IoT ecosystem, The Cloud Computing service model can add better business value to the IoT based Big Data and Analytics solutions.
The main architectural benefit is that the Edge Computing can do the filtering for the Cloud systems to focus on the usable data. The Edge Computing can do the heavy lifting for the Cloud systems.
Therefore, it is essential for the IoT solution architects and designers to understand the Cloud Computing service models leveraging the Edge Computing design constructs and lead the solutions considering these key technology components in an integrated way.
As architectural decisions, in order to obtain optimal performance, desirable scalability, higher security, higher availability, and lower costs, we must integrate the Cloud and the Edge Computing for the IoT and the Big Data Analytics solutions. These critical architectural decisions must be made at the macro design level and mandated for granular details at micro design levels in the solution lifecycle.
We understand that the Edge Computing can be a powerful extension to the Cloud Computing and consequently contribute to functionality, performance, scalability, availability, and security of the IoT and the Big Data Analytics solutions.
As IoT solution architects and designers, being aware of the capabilities of the Cloud technology stacks, the Edge Computing mechanism and their powerful management tools can be extremely beneficial in creating large-scale commercial IoT and Big Data Analytics solutions.
This knowledge, capability, and architectural governance consideration for integrated solutions is a technical leadership requirement for the IoT solution architects and designers to add desired business value to the commercial IoT and Big Data Analytics solutions.
The original version of this story was published in another platform under a different title.
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About the Creator
I'm a writer and published author with four decades of content development experience in business, technology, leadership, and health. I work as a postdoctoral researcher and consultant. My background is at https://digitalmehmet.com.