The increase in the popularity of the internet has enabled many internet-related services also to assert their importance. One of them is the Internet Of Things (or IoT). It is hypothesized that almost every device will be connected to the internet by the end of this decade.
In a world where every device needs the internet to connect and, based on the connection, perform the tasks which they have been assigned with, latency becomes everything. Read how edge computing is one of the trending big data technology you can’t ignore.
Latency is often called ping or the lag between the time taken from the time you give the instruction for an action to be performed and the time when it actually begins. If we are to follow the paradigm of cloud computing, the bandwidth requirement will be huge.
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Since all the resources are essentially controlled by one primary server and server to client communication is the key in cloud computing, either the time a task takes to complete would be huge, or the required bandwidth will be if we use cloud computing for IoT devices. Edge computing comes into the picture to solve this issue, and here is the by far the most noticeable difference between edge and cloud computing.
Edge computing is another way to approach the whole idea of the cloud ecosystem. Edge computing allows the computations to be done in real-time, thus effectively reducing the lag or latency issues whatsoever. In edge computing, all the computing is done as close as possible to the device, which is generating the data as opposed to a central system first collecting the data and then processing on the data.
To put things into perspective, let us take an example of a vehicle. If the car is tasked with calculating the amount of fuel it consumes automatically based on the data it gets from the plethora of sensors onboard, the computer behind this entire operation would be deemed an edge device.
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So, to improve the performance of these edge devices, reduce the overall latency in operations, and reduce the amount of bandwidth that is required to make the computation, edge computing was conceptualized.
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Since both edge and cloud computing aims to deliver the required resources over the internet, to find the underlying differences and choosing one over the other could prove to be complicated. Let us first make the two terms very clear to ease your pressure and then pit them head to head edge computing vs. cloud computing.
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Edge Computing
To really understand the definition of edge computing and appreciate the reason for its existence, we must first take a look at the need; why did we need an alternative to cloud computing? It started with the growth of Internet-enabled “smart home devices,” or as we know it as IoT was on the rise. Learn more about edge computing.
To enable or rather to provide computational capabilities to otherwise “dumb devices,” we had to connect them to the internet and install a number of sensors on them to gather data.
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The massive amount of data generated by each of these devices also needs to be processed to gain meaningful insights and really make these internet-enabled IoT devices smart. To do tasks on the data, in cloud computing, the data would have been collected, and since it revolves mostly around a central server, there would be a time delay on both the ends.
Time would be needed for the data collected by these devices to reach the central server to be processed, and the processed data would also take time in coming back to these devices hence introducing a latency.
Another critical thing is if the data which is collected is massive, then a good internet connection or an adequate bandwidth would be required to accomplish this task. Thus, edge computing becomes an all-inclusive term that takes some of the critical computations required and pushes them closer to the device or the edge.
Following a decentralized pathway, edge computing allows the required resources to be distributed among all the connected devices. If there is any need to collect data and do computation on the data in real-time, then edge computing should be preferred. The two main befits which edge computing brings to the table are the improved performance and low cost of operations.
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Benefits of using Edge Computing
1. Improved Performance
The collection of data to be transferred to the cloud, edge computing also takes the data, adds some processing to it, then analyses the information, find out the necessary actions to be performed on the data, and finally completes the action it deems fit. All of this obviously happens locally and in fractions of seconds (milliseconds), thus improving the performance.
2. Reducing the cost of operations
The main qualms that the tech community has with the whole cloud computing model are the extra costs associated with improving connectivity, migration of data, improving the bandwidth requirements, and reducing the latency. The edge computing model can easily counter all of these. When the edge computing model has used a continuum that goes from the device to the server is formed.
This continuum is very valuable because it is capable of handling huge volumes of data. You would not need to have the costly gigabit connection to ensure that the data generated from these devices are actually processed and reach their end goal. Most companies and organizations prefer the edge computing model because of the improved performance and reduced cost of operations.
Cloud Computing
Cloud computing is essentially the service that allows the user to use a plethora of services like software, Integrated Development Environment (IDE), storage, servers, even hardware like Graphics Processing Unit or (GPU), or even Central Processing Units (CPUs) over the internet. There are three main characteristics of any vendor which deals in cloud computing:
- The Services or the resources that they provide have to be scalable.
- There is pay for only what you need, meaning the user only has to shell out money for the resources that they need at that time.
- These vendors have to develop and manage the backend for the services that they provide.
Based on the services that they provide, there are three different cloud computing models, which are essential to know to understand the paradigm of cloud computing properly. You will find all the three models listed below:
1. Platform as a Service or PaaS
Platform as a service allows the users to buy their way into gaining access to the various platforms they can use to deploy their won software or the application they have developed to the cloud. Network access or even the operating system is not within the user’s control, and hence this can create some terrible constraints on the nature of work that you can accomplish.
2. Software as a Service or SaaS
In this model, the customer only pays for access to use the software which is already hosted on the cloud.
3. Infrastructure as a Service or IaaS
In this model, most freedom is given to the user. You can pay to get complete control over the operating system, the applications hosted on the cloud, and the cloud’s storage. All this can be easily accomplished without the service provider actually handing you the key to their cloud.
Read: Cloud Computing Project Ideas
Benefits of using Cloud Computing
Many challenges cloud computing faces, but that does not mean that cloud computing has no benefits to offer in the modern-day setting. You will find all the significant benefits of using cloud computing listed below:
1. Flexibility and Scalability of the services provided
The technological stack of cloud computing allows users to start out small, thus saving making cloud computing very cost-effective in the beginning. You will also not be limited to the small start you had; you can upscale and add more features anytime you want. Similarly, you can also remove the components you think are no longer needed (or downscale) very quickly.
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2. Cloud computing is very reliable
The services that use multiple websites have a fail-safe in any disaster. Thus, making cloud computing very stable.
3. Maintenance of cloud computing services
The ones that provide you with cloud-based services they only tackle their entire cloud care.
Performance and Latency: Improving Real-Time Computing
Due to the centralized architecture of cloud computing, data transmission to and from a remote server frequently results in latency. However, edge computing brings calculations closer to the actual devices, allowing real-time processing and cutting down on latency. IoT devices can quickly analyze sensor data from a connected car to determine fuel use by utilizing edge computing, all while guaranteeing low latency.
Cost Effectiveness: Cutting Connectivity and Bandwidth Costs
For the movement and processing of enormous volumes of data, cloud computing depends on reliable network access and ample bandwidth. When dealing with big IoT implementations, this necessity may result in higher expenses for businesses. However, edge computing reduces bandwidth usage by dividing up computational power among linked devices. Because there is less need for pricey high-speed connections, firms can save a lot of money thanks to this decentralized strategy.
Security Considerations: Juggling Edge Device Vulnerabilities and Protection
When opposed to cloud computing, edge computing offers more security concerns. The security of individual edge devices becomes increasingly important as calculations move closer to the devices themselves. Advanced security measures must be implemented to protect the distributed computing environment. As they are in charge of managing the underlying infrastructure, cloud computing providers, by contrast, often provide thorough security safeguards. So, cloud computing frees them from the responsibility of protecting individual devices.
Allocating Resources Efficiently: Meet Dynamic Demands
The unmatched scalability offered by cloud computing enables companies to easily scale up or down their resources in response to demand. Instead of making large upfront investments, businesses can allocate processing power, storage, and other resources as needed. Edge computing, on the other hand, provides localized scalability, allowing individual devices to adjust and allocate resources in a flexible manner. This flexibility is especially helpful when cloud connectivity is patchy or inconsistent.
The necessity for effective data processing has become critical in today’s linked world, where the Internet of Things (IoT) is booming. Two popular methods that meet this need are cloud computing and edge computing.
Difference Between Edge and Cloud Computing
The necessity for effective data processing has become critical in today’s linked world, where the Internet of Things (IoT) is booming. Two popular methods that meet this need are cloud computing and edge computing.
One essential thing to keep in your mind as we discuss the difference between edge and cloud computing is that edge computing is not designed to replace the cloud computing completely, and neither will it be able to. The comparison of both is like comparing an SUV with racing sports cars. Meaning both are very good in their respective departments, but doing an edge computing vs. cloud computing comparison is unfair. With that being said, you will find all the significant differences in the table below:
| Points of Difference | Edge Computing | Cloud Computing |
| Companies which should use either of the two | When you need low latency and the computational time has to be kept very low, edge computing is your ideal choice. So, companies that are neither too big nor too small and have budget constraints can easily opt for edge computing without giving it a second thought. | This type of computing really shines when storage is your primary concern. So huge companies that deal with cloud storage should opt for cloud computing. |
| The availability of coding and programming | You can use many different programming languages, all with having different runtimes. | Generally, only one language is preferred, as the entire cloud has to run on it. |
| Security | There a need for a very sophisticated plan of securing the computers. | There is no need to have a solid security plan. |
Also Read: Cloud Engineer Salary in India
When to Pick Edge Computing or the Cloud
Cloud computing shines when large-scale data storage, centralized processing, and resource scalability are essential. It is appropriate for online services, machine learning, and data analytics applications. Contrarily, edge computing is perfect for applications that call for distributed processing, low-latency response, and real-time decision-making. Examples of use cases are autonomous vehicles, industrial automation, and remote monitoring systems. Some businesses might even use a hybrid strategy that combines cloud and edge computing to get the best results.
Conclusion
At upGrad, we offer the Advanced Certification in Cloud Computing program. It lasts only for 13 months and is completely online so you can complete it without interrupting your job.
Our course will teach you the basic and advanced concepts of cloud computing along with the applications of these concepts. You will learn from industry experts through videos, live lectures, and assignments. Moreover, you’ll get access to upGrad’s exclusive career preparation, resume feedback, and many other advantages. Be sure to check it out.
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