![]() ![]() For example, in most of our mobile devices, we have either Apple’s Siri or Google’s Assistant feature. ![]() It is the time delay associated with running a particular process. These challenges included bandwidth limitations, latency problems, and privacy issues. Yet, with cloud computing, there came about three main challenges. Big technology companies such as Amazon, Microsoft, Google, and IBM are some of the industry players that identified an opportunity to offer these storage services and computation resources to businesses and individuals. Cloud computing gave us access to larger storage capacities and computational resources.įor instance, this enabled us to train machine learning models and store data that wouldn’t otherwise be storable on our devices. This data is accessible in real-time from your mobile devices, tablets, smartwatches, and laptops. With cloud computing, data storage and computational resources exist in the cloud. Then came the cloud computing era which became a game-changer. However, this type of computing was limiting as these devices could only hold so much data and had access to limited computational resources. All computations and programs ran locally based on the data, information, and processing power the computer had access to at that moment. Brief historyĬomputing used to be a process that you could solely and most commonly perform on your desktop computer or laptop. The figure below depicts the centralized nature of cloud computing:īut, before we venture further into edge computing, let’s appreciate what preceded edge computing. Due to this, it is easier to create real-time insights. This is in contrast to current practices where computation happens on centralized servers. With edge computing, the computation happens near the physical location where the data is collected. This isn’t ideal for real-time systems as they experience issues relating to latency, bandwidth, and privacy.Ī possible solution? Edge Computing. Yet, with most current systems, computation is centralized. A good example is the flight control system which receives information from different sensors. Real-time systems process data as the data is received, where a response is guaranteed in a stipulated timing constraint. Introductionīefore reading this article, a reader should be familiar with a variety of computing infrastructures and data storage resources such as Google cloud and Amazon Web Services as they are the de-facto standards in many industries today. This has led to a growing need for real-time data processing. There are vast amounts of data being collected from our mobile phones, autonomous vehicles, cell towers, and factories. ![]()
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