Edge computing aims to bring computing close to the data source to reduce bandwidth and latency usage. It means less computing work in centralized data centers and the cloud and more on local environments such as edge servers, IoT devices, and user computers.
When you bring computation to the network edge, the communication between the server and client happens at a close range. While cloud servers can be geographically distant from the devices they communicate with, the network’s edge is close to the devices it communicates with. When data is close to the users, the application’s speed is faster, providing a better user experience.
Instead of sending client data to a centralized data center or the cloud for analysis, processing, and storage, edge computing allows you to process that data close to the sources of data generation.
Computing can happen on the device generating the data or a data center closer to the data source. The server and the client communicate at a short distance, reducing bandwidth usage and latency and increasing communication and data processing speeds.
Say, for example, you want to scan your face for smartphone facial recognition. For a cloud computing-enabled facial recognition algorithm, data travels to a centralized processing location, which may not be close to the smartphone. With edge computing, the algorithm runs locally on an edge gateway, server, or smartphone, resulting in quicker response times.
Edge computing is part of an interconnected system comprising the cloud and network-connected devices processing data. This interconnection is known as the edge-to-cloud continuum. Edge systems work along this cloud continuum to achieve a secure, high-performant, cost-effective edge ecosystem. Though it’s not part of the edge, the cloud is part of the cloud continuum and helps to manage edge infrastructure and run enterprise applications.
Edge computing involves the following components and steps:
Edge computing provides many benefits, including those highlighted below.
With edge computing, there’s less data movement over a network to a central data center or the cloud for processing. Data processing occurs near the source, reducing latency, and enabling quick action and decisions based on current data. Jitter also reduces when there’s less data movement to and from the cloud or a data center.
Moving data parsed over the internet is a target for cyber attackers. Edge computing reduces the attack surface by ensuring less data parsing to and from a data center or the cloud.
Information in edge computing is on multiple devices, and less data is in the cloud. This contrasts with cloud computing and data centers, where most data are in a centralized environment—making it a larger, more accessible target. If someone attacks an edge device, there is less damage since more data is safely in other unaffected devices.
Computing at the edge also allows you to segment data based on the network’s physical environment and the purpose of the data exchange. With segmented data, you can apply authentication and security mechanisms to each data category.
Modern organizations deal with large amounts of data and require a high bandwidth network to support the transmission of high data traffic. But acquiring high bandwidth comes at a considerable cost, and computing in a data center leads to bandwidth costs because data must pass to and from the cloud.
Instead of sending data to the cloud or a centralized data center and incurring the costs of managing and storing this data, you can utilize edge computing to ensure that only processed data goes to these core environments. You minimize data transmission costs and ultimately reduce data processing expenses by processing data locally.
Additionally, because edge computing processes data faster than cloud computing, you can respond more efficiently to changing conditions and customer interactions. Edge-induced responsiveness supports reduced operating costs by mitigating delays.
You must analyze information in real-time and act quickly — especially during a crisis. Edge computing enables the provisioning of quality information at high speeds so that you can make decisions and act quickly.
Operations continuity is also necessary during crises that require you to work remotely. Edge computing ensures less traffic on the network when you shift to remote work by encouraging local data processing.
The resiliency of edge computing is especially useful if you have a poor wide-area network WAN or internet connection, as the systems’ distributed nature allows them to continue working in such conditions. Unlike the cloud, where an Internet connection is necessary for data processing, edge computing can process data locally without internet access. This allows computing to happen in remote locations.
Latency is lower in edge computing compared to a traditional centralized data center. You can leverage this low latency for fast data retrieval, real-time data analysis, and ultra-fast data storage. By taking minimal time to analyze data, you can make decisions in near real-time and respond to customers faster.
The cloud offers computing power for processing large amounts of data. With increased data generation at the edge, the bandwidth between the cloud and the edge device may become insufficient for data transmission. This may translate to prolonged response times when sending data to a centralized data center or the cloud for processing. Processing data at the edge means less response time since data doesn’t have to travel for processing.
Cloud computing can involve hybrid, private, and public clouds. The end users only pay for the storage, computing, and data transfer resources used. There are various pay-per-consumption cloud deployment models, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
With IaaS, you pay for the infrastructure resources that you use. These include cloud technology, storage, and computing resources. You can scale your computational platform up or down. PaaS allows you to use your software as your source for a third party’s cloud operating system, hardware, development tools, and middleware. Finally, with SaaS, you pay to use the existing applications and software from the cloud.
Edge computing also uses a pay-per-consumption business model, but its performance is greater than that of cloud computing. This is because it distributes computational nodes among locations and is close to the end-user. The computation capacity is near to end users, increasing the application’s availability.
You can scale up a cloud computing system network, processing, and data storage resources. This comes with additional costs for in-house infrastructure or a cloud subscription. However, you can reduce the cost of scaling by running custom logic at the edge and only sending necessary workloads to the cloud, making edge computing a more practical choice.
Edge infrastructure is scalable to optimize resource use and ensure workloads can move. Edge Compute providers are optimizing their infrastructure for running high-performant systems. Adding edge nodes over a network can scale an Edge Compute system. Edge Compute providers such as StackPath are providing an auto-scaling option so that you can create your workloads with auto-scaling ability.
