Edge computing has become the latest buzzword in the IT world. While a few years ago, cloud computing was the most popular term, now it has been replaced by edge computing. In this article, we briefly explore what edge computing means, its advantages and disadvantages. Let’s start with the basics:
Definition of Edge Computing
The research firm IDC defines edge computing as a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet”.
In simple words, edge computing means moving majority of the processes to local places whilst running only a few processes in the cloud. According to another definition, edge computing unlike cloud computing prefers to do processes on the closest physical location, for instance, on the user’s device. The computing is done ‘off’ the network/internet and is kept locally.
Edge computing is usually used with reference to Internet of Things (IoT) devices. As IoT has gained popularity in the last few years, edge computing is seen as a more suitable option than cloud computing. Instead of sending data to cloud centers or virtual data bases, edge computing allows IoT devices to process data near their closest location, i.e., at the edge of the network. Once the processing is done, all of portion of the data is sent to the central processing location
Role & Advantages of Edge Computing in IoT
Edge computing is beneficial for a number of reasons, when it comes to IoT devices. Here are a few reasons why:
Reduced latency
The time incurred in sending a query from the device to the network and getting a reply back is known as latency. Although smart devices have become more sophisticated over time, when you send a query on network – it still takes time because of multiple reasons: bandwidth limitation, network speed and distance of device from server/database. Although usually latency/time lag is unnoticeable, in some scenarios it is crucial. For instance, if the network is throttled and a self-driving car connected to it is unable to make a decision on the road – it can be disastrous. With edge computing, no such unfortunate scenarios can occur as the devices can compute and make decisions locally at a much faster and efficient speed.
Increased security
This does not come as a surprise that even the best of databases can be hacked. If a cloud database is hacked, it means millions of users’ personal information is compromised. This makes the user vulnerable. On the contrary, with edge computing not all of your information is sent to the cloud. Only important and processed data is sent. Because user devices are not always connected to the internet, edge computing also keeps their data safe even when the cloud database is hacked. Although edge computing does come with its own challenges, the technology does not transmit all the data of users to the cloud.
Enhanced reliability
Distributed Denial of Service (DDoS) attacks are not uncommon for cloud-based servers and services. A DDoS attack occurs when a server is flooded with artificial queries, making it jammed so that no real queries from the users can be answered. With edge computing, this situation can be avoided as the most of the information is stored locally. Secondly, as edge computing is not dependent on internet – slow servers or network failure will not have any affect on the users too.
Minimized operational costs
When the majority of data is stored and processed in the nearest logical location or ‘at the edge’, cloud storage is not required in abundance. Typically, in cloud computing, all the data is immediately saved in cloud – which means that quite a large cloud storage space is required for every user. And that comes with costs, of course. With edge computing, as most data is processed on the device and only the relevant data is transmitted on the cloud – it saves large amount of data storage. As a result, edge computing minimizes the operational costs.
Better performance
As discussed earlier, it takes some time for the query to be sent and replied back by the data server in normal settings. But with edge computing, as data is both stored and processed as close to its source as possible, it results in less lag time and thereof improves the performance of the application. This also means you can analyze the data in real-time, which is not possible otherwise.
Real-life use cases of Edge Computing
The fast speed and reliability factor that comes with edge computing makes it ideal for a number of scenarios and industries. Here are a few:
- Self-driving vehicles
- Smart video orchestration
- Power management with smart grids & smart meters
- Mobile app data management
- Traffic management
- Safety monitoring in remote oil and gas rigs
- Stock market trading
- Fleet management
- Gaming
Challenges of Edge Computing
Just like every picture has two side, edge computing has a few disadvantages too that include the following:
More hardware
When the data is not being sent to a cloud server and is being stored and processed locally, it means users need to buy devices that have more hardware. The good thing although is that as computing power of device and their storage space is becoming more compact and smarter, it is not a deal-breaker.
Security risk
While edge computing solves the security problems of cloud computing, it does make the device more crucial. If a hacker gets their hands on the IoT device, they can easily get access to all the information of the user. To solve this issue, a protection wall needs to be introduced to edge computing so that the data cannot be compromised.