When is Edge Computing a Better Choice than Cloud Computing?

05 Sep. 19
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As the Internet of Things and associated devices becomes more prevalent in our discussions around digital transformation, there are a lot of solutions being brought forward for how and where these IoT devices will be hosted and stored. While cloud computing is often recommended as a good solution, there is a new infrastructure concept that is proving as a better choice, called edge computing. These two solutions are sometimes discussed interchangeably as if they are exclusive to one another but utilizing one does not preclude the other as they function in different ways. The current impression is that edge computing is likely to replace cloud architectures altogether, however, it is more like that edge computing will be utilized in specific use cases where it delivers superior results.

What Is Edge or Fog Computing Exactly & How Does It Differ from Cloud?

In order to understand what edge computing is, we need to understand how it differs from the current cloud computing architecture. With cloud computing, all data is gathered and processed in one centralized location, most commonly a data center. Any devices or applications that need to access the data, must connect to the cloud-first, where it can then remotely access the data needed. The major benefit of cloud computing is that it can expand both storage and processing capacity as needed due to it being based on a scalable data center infrastructure system.

The fog computing or edge computing concept differs from the cloud in that it stores a vast amount of data or information on the outer edge of the network. Typically, with IoT devices, the data that is produced by them is relayed back to the data center, processed, and further instructions are sent back to the devices. The problem with this is that it takes time for the data to make its way to the data center for processing, and there is incredible strain on the bandwidth needed to send data back and forth between the edge and the center of the network. Where IoT devices are concerned, slow network latency can have serious consequences as these devices rely on the instructions that are relayed back. Edge computing solves this problem by relocating crucial data to the edge of the network so that edge-enabled devices can process and gather the data in real-time, allowing them to respond effectively and efficiently.

What Are Some Specific Use Cases Where Edit Computing is Superior?

While cloud computing does remain as a viable solution, there are some instances where this new edge computing infrastructure may be a more viable choice.

  • One example is with automation and self-driving vehicles. Autonomous vehicles will gather a tremendous amount of data from their environment and nearby devices in order to make split decisions. The vehicle’s reaction time is solely dependent on the instructions it receives back from its computing resources, which are located at the core of the network. Any delay in these instructions could be the difference between the correct choice and the wrong one.
  • As more content providers move into streaming their entertainment options, it can be difficult for cloud computing to keep up with consumer demands. A way around this is to cache popular content in edge facilities that are located closer to end-users, allowing for speedy access. This also allows companies like Netflix and Amazon Prime to expand their services without compromising their network’s current performance.
  • In situations where there isn’t enough network bandwidth or reliable network bandwidth to handle the amount of data that is being sent back and forth, edge computing can be a fantastic solution. This also works for a network’s that have poorer communication connections.
  • Any industry that requires an immense focus on cybersecurity, may have privacy concerns about using cloud computing to send data over public networks. As with any online access, it can be hacked or broken into. Edge computing is a better option in this case since all data is retained locally.
  • Any IoT application that requires rapid data sampling or requires calculated results without delay would work well with edge computing. This is the most common use case of fog computing, as a lot of IoT devices do not need data to be processed collectively and analyzed cumulatively. There is no point in wasting expensive bandwidth on transporting information that doesn’t require these steps. One option is to have sensors connect to the cloud when there is information that does need reporting, rather than always being connected.
  • Edge computing can even work with IoT deployments that require both localized and batched data processing. For instance, edge computing can be used in this way with a remote retail store that needs to tally up sales and inventory daily. The remote retail store does not need to send off data about every single transaction being made from minute to minute, but the headquarters does require a tallied report. With edge computing, real-time data of each transaction can be processed locally, then at the time of closing, a report can be generated and sent off through the network to headquarters. What problem does this solve? With current cloud computing architectures, real-time data is streamed into it and accumulated rapidly. This data is often seen as useless as it isn’t compiled in such a way that is useful to the business (i.e. reports), yet organizations don’t want to delete the data for fear that it can be used. This ends up wasting a ton of money as the data is almost always stored indefinitely but never utilized. Edge computing fixes this problem by sending only post-processed information to the cloud.

    While edge computing may be a better choice in some use cases, as seen above, it doesn’t mean that cloud computing should be done away with completely. It isn’t an either-or proposition, as these two network architectures can be used in tandem with one another to maximize the processing and use of data while minimizing limitations like unreliable connections, expensive bandwidth processing, and cybersecurity threats.

More than 75% of enterprise data will be processed outside the cloud and IoT edge computing will play a major role in it. The potential size of the edge computing market will increase by $13 billion worldwide within a period.

As per existing trends in IoT, most companies can consider edge computing for upcoming products to take full advantage of technology.

Investment in IoT based edge technology is surely the best bet for the future. It’ll help to channelize a better IoT ecosystem as well. Let’s talk and build best IoT system.

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