Edge computing is an emerging trend transforming how devices harness data for faster, more efficient performance. And it aims to reduce the time it takes to process requests and transmit data, resulting in faster response times and improved user performance. This technology reallocates certain processes from data centers to places closer to the source or where they are required, such as edge devices.
This blog is about how does edge computing reduce latency for end user by reducing the amount of data that needs to be transmitted over long distances. This post reviews the underline concept of edge computing by bringing the edge computing architecture compared to traditional methods.
Why there is latency for end user?
In simple terms, latency is the delay in data processing between two devices that are connected. So, why exactly does latency occur and what can be done to minimize its effects?
Latency can happen due to network congestion, slow processing speeds, physical distances between devices and even storage delays. While latency may seem like a minor inconvenience, it can actually have a significant impact on the end user’s overall experience.
In order to reduce latency, there are different approaches, such as enhancing network infrastructure, improving server performance, and increasing bandwidth capacity. In order to reduce latency, there are different approaches, such as enhancing network infrastructure, improving server performance, and increasing bandwidth capacity. However, with the increasing demand for real-time data processing, edge computing is becoming a more popular solution. Edge computing brings the data processing closer to the user, reducing the latency and improving the overall user experience.
The difference between cloud computing from edge computing
Cloud and edge computing are two different ways to handle data computing needs. Cloud computing involves storing data and apps on remote servers and accessing them over the internet. However, edge computing involves processing and analyzing data at the edge of a network, closer to where it’s produced, rather than sending it to a central location for processing. This means that edge computing is better and quicker for devices that need to process data immediately.
On the other hand, cloud computing is more appropriate for applications that require a lot of storage, like remote data backup and virtual data centers. Knowing the dissimilarity between cloud and edge computing will assist organizations in making knowledgeable choices about which option to choose according to their requirements.
The following table summarizes the difference between edge and cloud computing.
Cloud Computing | Edge Computing | |
Definition | Centralized computing model and provide the computing resources and services via internet. | Distributed computing model which is processed and analysed data near edge devices rather sending them to a centralized location. |
Location | Resources are normally located at the data centres which is far from end user. | Resources typically located near end user or nearby network infrastructure. |
Latency | Introduce latency since data need to sent to centralized locations for processing. | Since data processing happen in real times or near the source, the delay between sending request and receiving responses is minimal. |
Bandwidth | Need significant amount of bandwidth to transfer data. | Since data is processed locally bandwidth is not that big issue. |
Use Cases | Big data analytics, machine learning and AI | Autonomous vehicles, IoT devices and video streaming |
Understanding the architecture of the Edge Computing
The architecture of edge computing follows a distributed computing model made up of edge devices, edge servers, and the cloud. At the edge of the network, there are different components, including sensors, routers, and gateways which collect data from various sources like IoT devices, mobile phones, and wearables. You can refer to our previous articles for a more broad understanding of distributed computing.
Edge servers process and analyze data collected by edge devices, while performing tasks such as filtering, aggregation, and AI-based decisions. Centralized storage and further analysis of processed data are provided by the cloud.
The Main Components of the Edge Computing Architecture
Edge Device | The edge and IoT devices have special abilities. They can do things like analyze data, use AI rules, and store information. They don’t need the edge server or the enterprise region to help them. |
Edge Servers | Edge servers help to put apps on devices. They talk to the devices using special programs that are installed on each device. Thousands of edge servers check up on millions of devices. If more information is needed, data from the devices is sent to the edge server for extra study. |
Edge Network | Recent advancements in networking have introduced the concept of an edge network or micro data center. This serves as a nearby cloud that enables devices to communicate with each other. The key benefit of this technology is that it minimizes the distance data has to travel, resulting in lower latency and bandwidth concerns. |
Cloud | This region can store and manage data, as well as help you track and understand what the data means. It can be hosted in the cloud or on a computer in your office. |
What does the future hold for edge computing?
Edge computing has been improving quickly. New technologies and practices are being used to make it better.. Although it’s currently only used in specific situations, it’s anticipated that it will become more common and have even more potential use cases.
Wireless communication technologies, like 5G and Wi-Fi 6, will help edge computing work even better. Edge computing helps with things like self-driving cars and moving things from one place to another. It will also make wireless networks cheaper and easier to use.
The upcoming trend is expected to bring a significant change in the usage of the internet. It will lead to more utilization of edge technology and open up numerous new possibilities for usage. Although edge computing is presently limited to certain industries and particular situations, it is likely to become commonplace soon and transform the way we handle data and retrieve information.
