The concept of edge computing is nothing but computing in a very distributed environment. This consists of storage and computing power closer to the computer where it is quite essential for the sources of information. When it comes to cloud computing, data is routed via scattered data centers, but the data is not scanned; rather the cloud comes as an aid to everyone. There is a significant saving in the storage space and lag time.
If we compare to IOT technology, edge computing can be used as an alternative method for the computing fraternity. This is all about having access to the real-time data, extremely close to the source of data, which is called the channel’s “edge”. Instead of having a consolidated cloud or a database server or for that a data storage place, it is all about having virtual machines in closer proximity to the place where data is generated.
For instance, in the case of a vehicle measuring the consumption of fuel, there are sensors that provide data and then there are sensors that are dependent exclusively on the data provided. The machine that is executing this program is known as an edge computing system or in literal terms – an edge device. Since we can observe this shift in the procurement of data and management of it, we will look into the details of these two computing techniques and also delve into some of the merits that each of these techniques has to provide.
What is edge computing?
By using a unified computing infrastructure along with a transmission channel, various computing resources and communication technologies can be easily deployed. This is possible only because of edge computing. By leveraging the use of edge computing, enterprises can easily accomplish the requirements around computing.
Through edge computing, whenever there is a necessity to gather information or for a particular user to execute a specific activity, the same can be done on a real-time basis. If we want to know the major advantages of edge computing, then enhanced efficiency and reduction in operational costs are the two primary ones that are associated with edge computing. However, let us look at some of the other benefits of edge computing as well.
(Read to Know More: What is Edge Computing? – All You Need to Know)
Advantages of edge computing
While there has been the emergence of various IoT technology-based edge computing devices, and an increase in potential network attack vectors, there are many security benefits that edge computing can demonstrate. If we look at the conventional cloud computing model, then it is primarily centralized. This makes the whole setup vulnerable to exploitation. Through a variety of devices and cloud services, edge computing expends out computing and storage solutions and various apps. This makes it very difficult to take down an isolated instance.
One of the significant benefits of edge computing is its ability to enhance the productivity of networks by reducing any type of latency. The data that is accumulated does not have to travel a long distance, unlike the traditional cloud environment. This is because IoT edge computing devices can manage private data by accessing nearby edge data centers.
For many enterprises, speed is becoming very critical. For instance, the financial services industry cannot have any sort of latency. Having even a millisecond of delay can create a serious impact on the business. Another situation is the healthcare sector. One can’t imagine the serious impact on the lives of people if there is a snag in the machines and equipment that run the sector. Organizations that are working on a customer-centric model, can face customer ire if they don’t get the desired experience due to slow speed. Thus, speed becomes a critical factor for businesses.
Edge computing is not just about the collection of data. It also analyzes and takes necessary actions on the collected local data, over and above the data that is required to be transferred to the cloud. Even though these tasks are executed in a fraction of a second, still it is critical as it has a direct impact on the performance of the systems.
For instance, in the case of industrial plants, if these tasks are executed from isolated plants, then it can pose an obstacle for transmitting large volumes of data in a real-time mode. However, this issue is addressed by tagging intelligence tools and applications with the network edge. By using edge computing, analytics tools can be brought closer to the machine, thereby eliminating the middleman.
Reduction of operational costs
When we consider elements such as performance features, throughput, data management, and communication, cloud computing turns out to be a very costly option. However, edge computing has a very low bandwidth requirement and a very less bandwidth consumption, making it an extremely cost-effective option.
It is not easy for enterprises to forecast IT infrastructure requirements and the investment of having a dedicated private cloud instance will be exorbitant. Hence, edge computing provides a flexible option to scale up.
Having IoT edge computing devices along with cloud network infrastructure, which is located close to and available for end-users, reduces any risk of network failure or network issues in a faraway location.
Use cases of Edge Computing
There are multiple examples where edge computing is used, however, the three main areas where edge computing is used extensively is
- Automotive/AI-powered vehicles
- Smart Homes
What is Cloud Computing?
A simple definition of cloud computing is the utilization of different users via the internet. These resources include application development frameworks, storage systems, servers and other software as well.
There are three major features that any cloud service provider will deliver.
- Flexible services
- The cost of various services involving memory, preparation, and bandwidth is to be borne by the user
- The entire back end of the software is managed and administered by the cloud service providers
(Read to Know More: Cloud Computing | Ultimate Guide for Beginners)
Service Models of Cloud Computing
From a market standpoint, cloud computing models can be deployed in different types, depending on the requirement. The different services models of cloud computing are:
- Platform-as-a-service (PaaS): PaaS customers can access the platform and thereby implement the software and cloud applications. Things like internet connectivity and operating systems are not in the control of the user. This can result in some constraints on the scope of the software. Some of these examples are Amazon Web Services, Rackspace, Microsoft Azure
- Software-as-a-service (SaaS): SaaS this model, the right to access or use the cloud service, also known as a cloud-hosted application, must be procured by the user.
- Infrastructure-as-a-service (IaaS): IaaS, the customer can administer and monitor the operating systems, software, network access, and storage without managing the cloud per se.
Deployment models in cloud computing
Like virtualization techniques, cloud computing also has a set of prerequisites that are needed for a successful deployment. There are majorly four types of deployment models in cloud computing.
- Community Cloud
- Private Cloud
- Public Cloud
- Hybrid Cloud
Advantages of Cloud Computing
While we did talk about some challenges posed by cloud computing, earlier in this article, however, there are some key benefits that the model delivers.
The model is flexible because it allows organizations to start at a small scale and then grows faster. The best part is that scaling up and scaling down is very easy making the whole model easy to operate
Cloud service providers are responsible for and delivering system security and the process of recovering data
- Mobile Access
Cloud service ensures that users are connected seamlessly through mobile applications
In the cloud computing model, the cloud service providers ensure the maintenance of all the applications and services that they provide
Difference between Edge Computing & Cloud Computing
Now that we have seen what these two computing methodologies have to deliver, let us do a quick comparative check to see the disparity between the two.
|Factors of differentiation
|Multiple application programs may be running at different types at the time of development.
|The Cloud computing model utilizes a single programming language for the specific development of applications on cloud platforms.
|This requires a very exhaustive and comprehensive security standard, with sophisticated authentication methodologies.
|Cloud computing does not require extensive security.
|Applications that have a considerable bandwidth issue can go for edge computing making it a viable and optimum option
|Any application that is involved in a large amount of data processing can be done through cloud computing.
|The process of computing happens on the system itself. Mostly on the system itself, edge computing occurs.
|Here, the storage of applications takes place on the cloud server, such as Amazon EC2 or Google Cloud.
|A new machine can be linked to a network by building the network.
|A large chunk of data can be stored on the cloud server and then accessed via the internet.