Digital transformation, internet of things (IoT), software development, data analytics – all these have one thing in common: no conversation about them is complete without a discussion about where these applications and software are hosted. The “cloud” is, of late, considered as one of the best options for storage and analysis. “Edge computing” is a relatively recent buzzword, used in the same context. People are often under the impression that the two are mutually exclusive concepts – that edge computing would eventually phase out the cloud and its uses; that one has to pick a side in the cloud vs. edge computing debate. However, while the two function differently from each other, they complement each other more than being rivals.
What is cloud computing?
Quick recap for the uninitiated – cloud computing is the concept of storing, processing and analysing massive amounts of data on remote servers or “data centres”, usually over the internet. The data centres are typically located far away from where the data is being generated and collected, causing a time lag between collection and processing, or “high latency”. While the time lag is generally only a few hundred milliseconds, it makes a lot of difference in time-sensitive applications like autonomous cars, where real-time data is required for seamless functionality. Further, a large quantity of data travelling back and forth over the network puts significant strain on bandwidth. This can reduce the pace of data processing and transfers even more. This time lag could mean the difference between life and death in time-sensitive applications such as the autonomous car. The answer to this challenge is “edge computing”.
What is edge computing?
Edge computing works directly in contrast with the cloud: it moves the storage and analysis of time-sensitive data away from a data centre and closer to where the data is being collected. In short, it moves a part of the application closer to where the source of data or end-user is. It largely reduces the travel time of the data and allows the processing to happen in real-time, effectively reducing the latency to nearly zero. This is a boon for applications that depend on real-time data processing. However, edge computing devices are designed for quick processing on-site, and not data storage. Cloud computing, in contrast, is based on a scalable infrastructure, so it can expand its storage and processing capabilities as required, making it ideal for applications that are not time-sensitive.
Advantages and challenges of edge computing
- The primary advantage of edge computing is its reduced latency, as it provides more responsive processing of real-time data.
- It also prevents overload of the data centre and reduces the strain on bandwidth caused by the transfer of data to and from the centre.
- Further, edge devices are less dependent on connectivity, making it more reliable when connectivity is poor.
- Another major advantage is that edge devices are usually used to process data for a specific task, making it more focused and the data processed through them highly individualised.
However, edge computing poses some challenges which can largely be solved by the cloud:
- As more nodes or edge devices are added, managing deployments and monitoring the performance of the software on each node becomes increasingly difficult.
- It is also a challenge to use the edge when easy scalability or access to global data is required. These functions are more suited to cloud computing.
- Another major concern is that edge devices can be more vulnerable to security breaches and attacks than a centralised server. Hence, one has to be mindful while building an edge computing application, paying special attention to the security aspect.
How should businesses take edge computing ahead?
Use the edge and cloud smartly
A critical aspect to keep in mind is that edge computing generally doesn’t work alone. It needs to be complemented with data storage on the cloud. Time-sensitive data is better processed through the edge, but most other applications still require the cloud.
Reduce versioning whenever possible
Limit the number of deployments in edge computing infrastructures. As the number of nodes increase, software management becomes more challenging, and it is much easier if all the nodes run on the same hardware, software and firmware configurations. While this is not always possible, it is useful to keep in mind that lesser the variables involved, the easier it is to manage the software.
Be careful about security
As mentioned above, edge devices can be more vulnerable to threats than the data centres used in cloud computing. Hence, special care needs to be taken while designing the security system for such applications. Some possible measures include data encryption and access control. A business also needs to create an effective authentication mechanism and ensure that access is given to only authorised devices.
The edge is not magic
Edge computing is being touted as a big game changer and something completely new. However, this is not the case. It is just a new way of performing actions similar to we have been doing since before the cloud came into being. It is just a different way of thinking about what we call a “mobile application” or a “point of sale” device.
Edge computing, while a great way to process time-sensitive data in real-time, does not replace the cloud by any means. They complement each other and improve their effectiveness as well as the efficiency of applications when used together in a judicious manner. Cloud computing still remains one of the most optimal ways to store large amounts of data as well as process less time-sensitive information. Edge computing, with its faster data processing, is increasing in popularity due to its application in several IoT use cases.