Processing speeds are often vital for business applications. Data analytics platforms and AI tools require efficient collection and processing of data. This can happen through onsite servers or centralized cloud-based platforms. Another option, edge computing, is gaining traction in the business world. Edge technologies rely on distributed frameworks that process data near its source rather than at centralized physical or cloud servers. It can be useful for specific purposes, but companies need to deploy it carefully and use it to complement existing IT infrastructure rather than completely replace it. Here is a closer look at the edge technology and how businesses use it.
What is edge computing?
There is no precise definition of edge computing, but in general edge computing describes placing enterprise servers closer to the sources of the data they process. An edge system can utilize distributed computing power among many units, instead of a faraway data center as they do in many cloud platforms. Since the servers are nearer to the devices collecting or transmitting data, the act of sending and processing the data is more efficient and the data is less exposed during transmission. Edge computing was developed to help address the latency issues in traditional cloud computing and to make near real-time data processing and content distribution more efficient. In addition, as more users adopt mobile and Internet of Things (IoT) devices, the demand for edge cloud services continues to rise.
What is edge data?
Edge computing assets are responsible for processing the information from devices on the network. Unlike traditional data centers, which are in one centralized location, edge data processing relies on a distributed design. Architects and engineers place multiple edge nodes on regional networks, and these can interconnect with other edge networks to facilitate an edge cloud architecture. Edge computing systems can scale up and down to accommodate the changing needs of the network. For instance, if the IT team installs more IoT sensors and controllers in one area, they can also expand the memory and processing capabilities of the nearest edge nodes.
What is an edge device?
Edge devices are endpoints (entry or exit points) for data in a network. IoT sensors are examples of edge devices. They collect information from the physical world and transmit it in the digital realm. Industrial controllers are also edge devices, as are gateways, which can collect the data from all local devices, transform it and send it to the nearest server node for edge processing. Edge computing is meant to reduce the processing load on edge devices; as a result, edge devices can either share or offload the job of transmitting and processing information, increasing efficiency and reducing the lag between data sourcing and delivery. These advantages lead many enterprises to integrate mobile hardware and edge devices on their networks.
Why should you use edge computing?
Edge computing is especially useful for time-sensitive tasks. Since it improves processing speeds, edge technology offers near real-time responses. For instance, edge computing systems can process data from sensors on machinery and adjust the equipment in near real-time to maximize production or respond to potential safety issues. With this setup, the company can constantly ensure peak performance and automatically avoid safety issues. Edge computing also helps manage massive volumes of data since it doesn't need to traverse the network back and forth from the edge to the cloud. Systems and organizations can also benefit from reduced bandwidth requirements. For instance, hospitals can use edge processing to handle data from the many pieces of medical equipment, sensors and computers within their facility.
Benefits of edge computing
Edge platforms offer several advantages. Here are four examples of benefits to businesses that use edge computing.
- Near real-time data processing is possible due to the proximity of the edge data centers to the devices producing the information.
- Scalability is also important for companies as they add, remove and manage multiple IoT devices on their networks. Decentralized edge nodes and gateways let the IT team scale up or down quickly to accommodate current data processing needs.
- Privacy improvements can be especially important for organizations that need to balance customer data with privacy concerns.
- Improved customer experiences can come from faster processing when using location-based features on apps or making payments. Edge processing limits the need to rely on cloud-based servers and any issues associated with them, such as the high cost of data transport and unreliable connections.
Drawbacks of edge computing
While edge computing is an excellent option in some use cases, certain challenges can make these systems impractical in other instances. For example, the cost of purchasing and deploying compatible hardware, software and other necessities can make edge computing expensive for many businesses. In general, edge solutions are best for larger enterprises with the teams to design and maintain systems and the capital to invest in IT infrastructure changes.
Edge computing models
Companies can rely on different edge computing models depending on their operations and the processes that are most important to them. Here are the most common versions:
- Edge AI calls for algorithm-powered analysis directly on edge devices or computers directly connected with sensors on vehicles or machinery. This setup allows immediate response to data inputs or sensor readings.
- Decentralized edge computing relies on localized edge devices and nodes to process data and make decisions. Industrial automation often relies on this setup.
- Public Multi-access edge computing (MEC) places mobile base stations in strategic locations to support data-intensive applications on local networks.
- Private Mobile Edge Computing can also be deployed on large campuses, inside large warehouses, factories, and other locations where low latency is essential. Edge computing as a service (EaaS) offers edge resources to companies on an as-needed basis. Third-party providers maintain the edge resources and offer access to client companies.
Examples of edge computing in action
As more consumers and companies begin to rely on technology for daily tasks and business processes, examples of edge computing are becoming more prevalent in all industries. For instance, retail chains might rely on edge computing at their stores for coordinated inventory tracking and payment processing. 5G edge architecture can deliver advanced features on a wireless network to every store location with ease. Many companies may also seek IoT edge computing solutions so that they can add more devices to their networks without creating bandwidth problems. In contrast, a traditional cloud platform model would call for all branches or devices to send data via wired, wireless data connections to a central platform where it would be stored and processed.
Edge computing applications
The benefits of edge computing are most clear for specific applications. Here are some of the most common uses for edge platforms.
- Smart buildings and smart cities can rely on edge computing to process data from IoT networks quickly with minimal bandwidth usage.
- Healthcare facilities rely on edge data processing to quickly analyze and store patient data.
- Financial services providers can use edge computing to improve the speed of payment processing and fraud detection software.
- Energy management companies can benefit from edge computing systems to obtain near real-time data on local energy needs and usage. These insights can make grid management easier and reduce the risk of overload or blackouts. Ultimately, edge computing is useful for any company needing, low-latency data processing.
Precursors to edge computing
The development of several different technologies provided the framework for edge computing.
- Client-server models introduced the idea of users accessing additional IT resources via servers. Edge computing puts such resources closer to endpoints and data sources.
- Wireless Sensor Networks (WSNs) relied on sensors to collect data, which was partially processed by devices or gateways before being sent to central servers. Edge computing uses a similar strategy but has more processing power for devices and gateways.
- Content Delivery Networks (CDNs) sought to decrease lag by placing local area servers near to users to reduce latency when sending data-intensive media. Edge architecture similarly reduces lag by placing processing systems closer to data sources, but edge systems can utilize a mobile network. The development of mobile devices, which rely on applications for on-device data processing, has also helped advance edge technologies.
Outcomes
Edge computing outcomes depend on architecture, edge device placement and other factors. However, with an effective setup, edge computing can produce the following results.
- Edge computing can reduce network latency by up to 30%.
- Edge computing lowers the risks associated with transmitting data over long distances, but it also introduces complexities to the IT architecture that may create vulnerabilities if not properly protected.
- Edge computing improves reliability because local devices can still connect to localized edge infrastructure even if they cannot get a reliable internet connection. Edge computing is also effective at reducing bandwidth usage. Devices can transform and compress data before sending it across the network, reducing congestion.
The future of edge computing
Two major trends in IT will affect edge computing in the future. 5G can further reduce latency in networks and provide edge devices with the high speeds necessary to support near real-time analytics and automation. This development will support the development of new applications for edge computing. Also, continued development of AI will allow devices to use local resources to automate tasks and train algorithms without relying on connections to cloud platforms.
Alternatives to edge computing
Depending on the needs and size of a company, edge computing alternatives may be a better fit.
- Fog computing brings cloud platform features to the edge of the network, so they are closer to end users. This option may be best for users with a tighter budget.
- Distributed computing uses a network of connected devices to perform complex tasks and collect and analyze data together. While edge computing brings near real-time data processing, smaller firms can get the speed they need using existing hardware for distributed computing or relying on third-party services.
- Serverless computing allows developers to run specific functions on an infrastructure provided by a third-party service. Serverless models may be the best option for companies needing very specific functions, such as processing data from sensors. Edge computing will continue to evolve. With the growth of IoT, AI, automation and mobile technologies, edge services will continue to be a vital part of companies' IT architecture in the future.
