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As the number of connected devices in the world is growing, companies have to think about how to improve the quality of the data from these devices. This is where cloud computing may step aside as most of the new data capabilities lie at the “edge.”
The ability of edge computing to bring devices closer to the data source, minimize response times and eliminate latency is highly beneficial and offers companies a new way of managing and processing their data. Learn the basics of edge computing in this article.
Defining edge computing
Edge computing implies processing of the data at the edge of a network (near the data source or near the place of creation) as opposed to centralized processing in a cloud. Speaking of the network edge, this is where a device or a local network connects to the Internet. The edge is near the devices with which it communicates and is the point of entry to the network. There are 3 main elements of edge computing:
- Device edge: where the edge devices reside;
- Local edge: includes the infrastructure to support apps and network workloads;
- Cloud: the node of the environment where all the data comes together.
Examples of edge computing include wearable devices, computers that analyze traffic flow, the Internet of Things, streaming video optimization, and various urban and global network access devices.
Due to the processing and data storage at the “edge”, edge computing provides reliable real-time data, reduces operational costs, improves response times and speeds up operations. But how does edge computing compare to cloud computing and what are the biggest differences?
Edge vs cloud computing
The main difference to remember is that cloud computing is about running workloads in the cloud, while edge computing is about running workloads on edge devices. But obviously, the difference in the approaches towards data processing unveils many other differences between these two computing paradigms.
| Edge computing | Cloud computing | |
|---|---|---|
| Reliability | Processing data close to the user (with and without Internet access) makes edge computing more reliable, as the risk of security threats and network failures is reduced | Access to the internet is required at all times to run in the cloud and software bugs can lead to poor performance |
| Speed | Immediate, real-time data processing | Maximum data processing speed in the cloud or on a vendor’s server |
| Performance | Require more bandwidth, reduce the burden of data traffic, reduce latency and provide reliable performance | Require rapid resource changes to meet computational needs, optimal cost of cloud services, provide reliability, and scalability |
| Security | High, because the data are stored at the edge, where confidentiality is very sensitive to security threats | High, because of the implementation of advanced cybersecurity measures by vendors and organizations |
| Scalability | Scalability Reliable infrastructure is needed to scale the edge network on time | Scaling is fast and easy with no downtime or failures. |
As we already said, the biggest difference between the two technologies is where the data is processed. Cloud computing is centralized, which means that data processing takes place in a cloud. While edge computing is decentralized, meaning the data processing is moved from centralized servers to the edge of the network or a cloud, near the data source.
One may ask, what computing paradigm is better? There is no definite answer to that, since it will heavily depend on your business needs and kind of data that you manage and process. If companies are working with big data that is not time-sensitive, it is better to use cloud platforms. However, when it comes to multiple devices, where the focus is not on the volume but on real-time data processing, then using edge computing may be a better solution.
The problems that edge computing solves
We already briefly mentioned the benefits of edge computing – now let’s look at them in more detail and see what issues this computing paradigm may resolve.
Bandwidth
The process of transferring data to the cloud requires a lot of power and high bandwidth. However, networks have limited bandwidth and there is a finite amount of data that can be transmitted over the network. Edge computing helps companies reduce bandwidth use and costs because large amounts of data are processed locally, closer to the source.
Latency
The greater the distance between where the data is created and where it is processed, the lower the processing speed, or latency, is. Edge computing can eliminate the latency problem because it ensures that there is no discontinuity in real-time processing and thus creates a more reliable network.
Security
Even though edge devices are still vulnerable to hacking, the decentralizing edge computing approach eliminates many of the security drawbacks associated with centralized data centers. Edge computing providers can develop a multi-layered security strategy. And given that edge computing can process data across multiple nodes and even devices, it enhances and strengthens data security and privacy.
Cost
As the amount of generated data grows, the cost of moving that data increases as well. Edge computing can help companies reduce costs, or at least keep them from rising, by reducing the amount of data being moved to and from the storage and processing center.
Reliability
Edge computing can process data locally, without the need for constant Internet access which greatly impacts the performance of the system or an application. As well, edge computing improves fault tolerance since the failure of one edge device will not affect the performance of other devices.
Edge computing use cases
Due to its benefits, edge computing is suitable for almost any use case – below are the most common ones:
- Smart homes: since smart homes rely on IoT devices that 24/7 collect and process such data as temperature or humidity, edge computing helps process this data with no delays. Hence, smart home equipment can immediately react to any trigger such as a change in temperature.
- Smart cars: due to the ability of edge computing to process data in real-time with low latency, this paradigm contributes to safer autonomous driving since cars will immediately react to any situation.
- Patient monitoring: real-time data processing contributes to more efficient patient monitoring and allows medical professionals to immediately react to any warning indicators.
- Manufacturing: edge computing helps distribute sensors throughout the manufacturing plant, providing data on how each component of a product is assembled and stored, as well as how long the components stay in stock.
Final thoughts
Edge computing has gained notice with the advancement of IoT devices so it’s safe to say that further evolution of IoT will also impact the way edge computing is implemented and adopted. As well, the adoption of edge computing requires companies to carefully estimate their data management processes to ensure maximal security and efficiency – so before you rush into adopting this computing paradigm, evaluate whether you have all necessary resources to make the most out of it.
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