How Edge Computing Is Revolutionizing 5G Technology
Table of Contents
Explore how edge computing revolutionizes 5G technology. Faster data processing, reduced latency, and enhanced user experiences. Discover the potential now!
Edge Computing Terms and Definitions
Edge
It highly depends on the use cases. Like in telecommunication, it may be a cell phone or cell tower.
Similarly, in the automotive example, it could be a car.
In manufacturing, it could be a machine, and In the Information Technology field, it could be a laptop.
Edge Devices
A device that produces data is an edge device like a machine and sensor or any device through which information is collected and delivered.
Edge computing
Edge computing is a distributed information technology (IT) architecture in which client data is processed at the edge of the network, as close to the originating source as possible.
Need For Edge Computing
- Powers the next industrial revolution, transforming manufacturing and services.
- Developed due to the exponential growth of loT devices, which connect to the internet for managing information over the cloud.
- Optimizes data capture and analysis at the edge to create actionable business intelligence.
- Promotes an agile business ecosystem that is more efficient, performs faster, saves costs, and is easier to manage and maintain.
The Importance of Edge Computing for 5G
One of many examples is smart home appliances. Yes, they are using low-latency computation near the requests. That is how you can control your home appliances using only one device. All of them are connected then the edge computation allows them to work on the requests faster.
Another example is cloud gaming and autonomous car. You may think that cloud gaming means that it is strictly using cloud computing. These games need the edge to improve their users’ gaming experience.
Key requirements of Edge Computing in 5G
There are four key requirements for the successful-
Deployment and operation of edge computing in 5G. While all four key requirements are important, achieving a balanced trade-off among them must be considered depending on the applications. Firstly, real-time interaction, which is the fundamental motivation for the use of edge computing over cloud computing, ensures low latency to support delay-sensitive applications and services (e.g., remote surgery, tactile internet, URLLC, unmanned vehicles, and vehicle accident prevention). A diverse range of services, including decision-making and data analysis, can be provided by edge servers in a real-time manner.
Secondly, local processing is feasible since data and user requests can be processed by edge servers, rather than the cloud. This means that by reducing the traffic amount across the connection between a small cell and the core network: a) the bandwidth of the connection can be increased to prevent bottleneck, and b) the traffic amount in the core network is reduced.
Thirdly, a high data rate is necessary to transmit the massive amount of data generated by a diverse range of applications (e.g., virtual reality and remote surgery) to edge clouds.
Fourthly, high availability ensures the availability of cloud services at the edge. Since edge computing pushes data and application logic to edge clouds, the availability of edge clouds is important.
Application of Edge Computing in 5G
Many applications of 5G are relying on edge computing for real-time interaction, local processing, high data rate, and high availability, including:
• Healthcare, such as remote surgery and diagnostics, as well as monitoring of patient vital signs and data. Doctors can use a remote platform to operate surgical tools to save a life from a distance where they feel safe and comfortable.
• Entertainment and multimedia applications, such as streaming HDTV or 3D TV.
• Virtual reality, augmented reality, and mixed reality, such as streaming video content to virtual reality glasses. The size of the glasses can be reduced by offloading computation from the glasses to edge servers.
• Tactile Internet, which is the next evolution of the Internet of things, provides ultra-responsive and ultrareliable network connectivity to ensure the successful delivery of real-time control messages and physical tactile experiences remotely.
• URLLC, which ensures high reliability between UE
s specifically in machine-to-machine (M2M) communications, supports low latency transmissions of small payloads with very high reliability from a limited set of UEs, such as fire alarms.
• Internet of things, such as smart appliances that connect devices (e.g., household appliances) to the Internet.
• Factories of the future, such as smart machines, to improve safety and productivity. Operators can use a remote platform to operate heavy machines, particularly those located in hard-to-reach and unsafe places, from a safe and comfortable place.
• Emergency response, whereby different kinds of data and information about an event or incident are gathered from different sources at different times. The partially available data and information are used to make critical decisions, and they provide a more complete picture of the event as time goes by.
• Intelligent transportation system, whereby drivers can share or gather information from traffic information centers to avoid vehicles that are in danger or stop abruptly, in a real-time manner to avoid accidents. In addition, unmanned vehicles can sense their surroundings and move safely in an autonomous manner.
Roles of Edge Computing in 5G
There are six main roles of edge computing to support real-time and interactive applications and services as follows:
Local storage– Edge computing unloads a massive amount of data from UEs to edge clouds. While edge servers offer distributed local storage for a significant amount of data. For instance, short storage provides temporary data storage to a set of interconnected mobile devices.
Local computation– Edge computing unloads computation and processes from less complex (e.g., smartphone) and highly complex (e.g., surgical tools and smart factories). The outcomes of the computations and processes can be valuable inputs to other UEs, such as those in a smart factory.
Local data analysis– Edge computing processes and performs critical and real-time data analysis on a massive amount of raw data gathered from different applications to generate valuable information. The capability to make data analysis locally reduces the latency required to send data to, as well as to wait for responses from, the cloud. Subsequently, the outcomes of the local data analysis are used for decision-making.
Local decision-making– Edge computing helps entities to make real-time decisions and corresponding actions in an automated manner based on well-processed data. The capability to make decisions locally reduces involvement from more components and data or information exchange, leading to
a) Improved system availability, particularly the cloud; and
b) Improved bandwidth availability.
As an example, edge computing facilitates local decision-making through automated factories. Multiple entities can collaboratively make decisions.
Local operation– Edge computing enables remote control and monitoring – particularly critical devices including those under unsafe environments – from a distance or a more comfortable or safer place.
Local security enhancement– Edge computing serves as an additional layer between the cloud and connected devices to improve network security, including UEs with limited resources. The edge clouds can serve as secured distributed platforms that provide security credentials management, malware detection, software patch distribution, and trustworthy communications, to detect, validate, and countermeasure attacks. The advantage is that, due to the proximity of edge computing, malicious entities can be quickly detected and isolated, and real-time responses can be initiated to enhance the effects of the attacks. This helps to minimize service disruptions. In addition, the scalability and modularity nature, as well as the capabilities, of edge computing can facilitate the deployment of blockchain among UEs with limited capabilities.
Conclusion
In this article, the key requirements of edge computing are to provide real-time interaction, local processing, high data rate, and high availability. Edge computing improves network performance to support and deploy different scenarios, such as remote surgery.
Edge Computing is very promising and has found many useful applications.
Bringing computation to the network’s edge minimizes the amount of long-distance communication that must happen between a client and server.
However, there are still many challenges faced by the community, ranging from fundamental technologies to novel application scenarios and potential business models.
Edge computing will become more prevalent because:
Data volume grows exponentially.
Computing power increases rapidly.
The Internet of Things will be omnipresent.
Computing will become more distributed.
The need for fast-response computing.
Edge computing will create many real-time applications.
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