Due to the emergence of IoT (Internet of Things) and 5G technology, Edge Computing is gaining more prominence in the field of technology by supplementing Cloud computing. It is predicted that Edge Computing will be at the forefront of all technological innovations in the future. In the wake of IoT applications and real-time data, the process of computing has to be much faster, and Edge Computing helps to handle those challenges easily.
What is Edge Computing?
Edge Computing is the latest paradigm technology in computing that makes the process of computing and data storage very much closer to the source of data where it is generated. Also, it helps to quick response and faster processing of the data at the generating source.
Here the Edge is about handling data closer to where it's being created, facilitating the process of data at greater speeds and volumes, and helping to superior action-led outcomes in real-time. Notably, this computational processing (edge computing) and data storage technique would help to be closer to the source of the device where the data is generated and used. It is a better option compared to the cloud as in the cloud, the data is stored in a distant location which takes time to process the data in need of time.
How Does Edge Computing Work?
In conventional networks, the data is produced in our systems or the client’s application. Then it will be transferred to the server, or the data centers located somewhere using the internet, intranet or LAN etc. There the data is stored and processed. This is a typical scenario of how computing worked in traditional settings.
As the volume of data increased and more devices were connected to the internet, it was really difficult for conventional data centers to accommodate all of them. Besides, this large quantity of data put immense stress on the internet and caused disruption and congestion.
Here comes edge computing as a solution. The concept is quite simple- without taking data into the data center, the data center is brought close to the data. In edge computing, storage and data processing devices will be deployed close to the devices where data is generated. Then the computing process takes place there itself.
Early computing: The applications were run only on a single isolated system
Personal computing: The applications were run locally either on our system or in a data center somewhere
Cloud computing: The applications were run in the data centers and processed through the cloud system,
Edge computing: Here, the application runs either close to the system or on the network edge.
Why do we need Edge Computing?
- It enhances computing operations more efficiently and effortlessly. And it is highly beneficial to be utilized in a remote location with less cost.
- Some Smart applications and devices demand real-time data which is only possible through Edge technology. For example, autonomous cars require instant response to ensure their smooth functioning, in this purpose Edge computing is the only solution instead of cloud computing.
- It ensures complete security for the user, which means it has the potential to process data without even putting it in the public cloud.
- It helps to Connect a device to a network from a remote locality. So, users easily handle their data and information.
- Through this faster computing method, we can achieve greater employee productivity.
Components of Edge Computing
Edge computing consists of components such as
An edge device is a particular piece of equipment that computes data and is attached to your device. As normally data processing takes place in a centralized data center, it occurs at the edge of the network through edge devices. Sensors, smartphones, and cameras are some of their examples.
Edge servers are another significant component of edge computing. They are located at the edge of the network and are situated very close to the edge devices. These servers process data and offer services to edge devices. Edge servers send data to the cloud before collecting and filtering it.
As the term suggests, it provides gateway functions. These devices integrate edge devices into the network. Edge gateways act as a bridge between edge devices and the cloud. In essence, edge gateways manage the communication between the edge device and the cloud.
Though edge computing is being developed to reduce the dependence on the cloud, it is still a vital component of edge computing. The cloud is used as a repository for ML models, applications, data storage, etc.
Benefits of Edge Computing
Edge computing offers low latency for devices as the data is processed and stored at the location where it is generated. Whereas in Cloud, it is stored in a distant location, and relatively high latency is caused there. For instance, virtual assistants which work in the cloud take time to process a request as the data is stored in a faraway place. However, it is quick and fast with the help of edge computing to process data within no time as it is stored in a nearby location.
Minimum bandwidth cost
Nowadays, our life is built on smart IoT devices which generate lots of data that is transferred to distant cloud storage centers. This processing demands a high amount of bandwidth and expensive cloud resources. In such a context, Edge computing is an answer to save your purse as here the data is stored and processed locally. Thus, it costs less in bandwidth.
Reduced Traffic in the network
It is believed that more IoT devices would be available in the market which generates more data and transfers the data to generate cloud storage centers. Indeed, there will be hectic traffic in the network which results in increased latency. In such a scenario, Edge computing can ease the riddle by storing the data locally and making it available at ease within a short span of time.
Edge computing and cloud development have made it easier for businesses to monitor their performance than ever before. The calculation, storage, and analytics capabilities are progressively being incorporated into devices with shorter footprints that are positioned closer to end users.
Transferring a large amount of data cannot be seen only from a technical angle. Its journey beyond regional and national boundaries raises concerns about data security, privacy, and other legal issues. In this scenario, edge computing helps us keep the data close to its source and within the limits of our domestic data sovereignty laws.
Edge computing is cost-effective as it saves server resources and bandwidth. Suppose if you decide to use cloud resources to support several devices at your home and office with smart gadgets, it becomes expensive. But edge computing can reduce the cost by moving the data processing and computational functions of all the devices to the edge.
Challenges of Edge Computing
In conventional networks, entities would provide higher bandwidth to the main data centers and lower bandwidth to the endpoints. But for edge computing servers, higher bandwidth is needed at all endpoints of the server. This demands more bandwidth compared to traditional networks.
Data security is a critical concern in the edge computing model. In centralized computing models, data is a secured computing model. In centralized computing models, data is secured. But in the edge computing model, every endpoint can be a vulnerable point. It is really difficult to standardize physical and technical security on remote servers like centralized data centers. To ensure security, the IT team will have to outline user access across a large number of edge devices. It is a bit difficult to handle.
The latency issue is another challenge in edge computing. Since the computation takes place close to the data in edge computing, latency can normally be reduced. However, because of distributive computing, the data transverses back and forth in each direction. It also has to share data with edge devices and handle access rights. This causes latency issues.
Data plays a pivotal role in business. Gathering data at the edge is a huge risk and may encounter legal hurdles unless we fail to meet the standards of the existing data handling rules. Additionally, innovative techniques have to be derived to transfer huge quantities of data, which is impossible now.
Difference between edge computing and cloud computing
Edge computing and cloud computing are two different models of managing data processing, storage, and transmission. The stark difference between these two lies in where data processing occurs. In cloud computing, data storage and processing happen in data centers that are located remotely from where the data is generated. This model was quite successful and effective for quite some time. But it brings in some limitations, specifically related to the case of IoT devices, which produce a large amount of data.
IoT devices have become an integral part of our lives. They have come into several applications, like smart homes, industrial automation, etc. These IoT devices produce a vast amount of data, and this data must be processed, analyzed, and responded to on a real-time basis. In traditional cloud computing, this data is sent to the remotely located data center for the whole process. This can cause several issues, including high latency, security concerns, and high costs.
However, edge computing is a decentralized model of computing that processes the data close to where it is generated. It involves processing the data locally on the device or on a nearby server without sending it to a remotely located data center.
Use cases of Edge computing
Autonomous cars are self-driving cars that run with the help of data and without any external help. In fact, here the data processing has to be quick and any delay in response would possibly affect its functioning. Since Cloud computing takes time to process data, edge computing is the apt technology for autonomous vehicles.
Many smart IoT devices such as diabetes monitors, Heart monitoring smartwatches, and fitness trackers, can monitor the health status of a person. These devices have to collect real-time data to process it faster for analysis. Slow data analysis is of no use in this scenario. Here, Edge Computing could be invoked to get Realtime data and quick analysis.
Since Edge computing could respond within a short span of time, which is immensely helpful with a view to offering a better surveillance system. Besides, security systems would also be capable of detecting any security breach and informing the user of that action.
Cloud gaming is a new type of live-stream gaming that is performed based on latency. So, edge computing could be used to ensure low latency and better performance.
Content management could be enhanced by storing content such as music, video, audio, photos, etc. Thereby providing a better experience for the user to manage their larger bulk of data easily.
Traffic management could be made easier with the use of Edge technology. Controlling additional lanes and managing vehicle flows are some of its examples that could be implemented with the use of Edge computing. The requirement of transferring a large amount of traffic data to centralized storage would not be mandatory with the use of Edge technology.
The idea of smart homes relies on IoT devices that gather and process data regarding their functions. This data is transferred into distant storage which results in many issues regarding safety, latency, and cost. Bringing it closer to the home itself through edge technology can address these issues.
Edge Computing technology offers more benefits compared to other traditional networks which are going to play a vital role in development. As more IoT devices enter markets, it provides more space for the technology to flourish in the coming future.