Edge Computing v. Cloud Computing

edge computing

The Internet of Things is expected to grow rapidly in the next year. In fact, predictions by Gartner say there will be 20.8 connected devices and used worldwide by 2020. From manufacturers to marketing professionals, everyone is jumping on board the IoT trend.

This growth has lead to a challenge with getting data processed from so many connected devices. To overcome that challenge, we have seen the growth of cloud computing and most recently the introduction of edge computing.

Before going too far into the changes to data processing, we need to talk about what edge computing and cloud computing actually do. Cloud computing has been defined as the centralization of processing and storage to provide a more efficient and scalable platform for computing. Edge computing is similar to cloud computing except it processes data and storage closer to the devices that use that data. Edge computing would be used in specific cases that help data become more applicable.

Differences between the Two

Now that we have the definitions squared away, let’s discuss the differences between the two. Basically, cloud computing is a broader concept that is widely used for all types of computing and data processing. Edge computing is a more tactical approach to data transfer.

The goal of edge computing is to process data near the device so that the device can grab that data quickly and use it in a more efficient time frame. Reaction time is a key value of the Internet of Things and the need to constantly send data to the cloud prevents that quick reaction time.

Cloud computing is good for data that is not time-sensitive or is not needed on a specific device at a specific time. Cloud computing is good for general-purpose platforms, while edge computing is good for purpose-built systems and devices.

Benefits of Edge Computing

Processing data close to the device and at the edge of the network has many benefits. These benefits include reducing the delay before a transfer of data begins, making the response time between applications and the device quicker and taking a load off the data center that is needed for cloud computing.

Considering the amount of data being consumed and supplied to the Internet of Things, it is crucial to use and supply that data somehow. Edge computing offers somewhat of a solution by attempting to screen incoming information, processing data on the spot and sending it directly to the user.

Edge computing has been alleged to lower costs and provide a smooth flow of service. For businesses, edge computing can lower IoT costs and gets the most value out of data transfers. Transmitting large amounts of data can be expensive and often strains the system and the data centers. By processing data near the source, edge computing helps combat some of that strain and expense.

Edge and Cloud Compliment Each Other

There have been reports in the news and by pundits that edge computing will replace cloud computing. This has been widely disputed with others saying both means are very different and neither can replace the other.

For example, it makes sense to have an edge computing application that is able to quickly process information and data, however, there is the need for cloud computing to ensure the edge would not be disrupted and unmanageable with data that does not need to be processed quickly.

Edge and cloud computing must work together. Edge computing can easily work with purposed based systems on information that needs to be processed quickly. Cloud computing can work with general-purpose platforms.

Using Both Computing Methods

Today the Internet of Things is huge and growing rapidly. With so many connected devices there needs to be a way to process data both quickly and efficiently. Cloud computing and edge computing can be used to process data.

While both offer different services they compliment each other. With edge computing working with specific systems to quickly process information close to the device and with cloud computing, working with general platforms to process large amounts of information through data centers.

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Nathan P. Sykes

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