Edge Computing and Cloud Computing: Exploring the Key Differences

The rise of cloud computing has opened up a world of opportunity. However, that’s not the only form of remote computing. Cloud computing has a lesser known cousin called edge computing. While there are similarities between the two concepts, there are stark differences in how they work and the purposes they serve.

However, together these two forms of remote computing are transforming the way we work, communicate, play and the landscape of society at large. Let’s dive into the world of remote computing as we compare and contrast cloud and edge computing.

Key differences between Edge and Cloud Computing

These are both forms of remote computing. Therefore, a useful starting point is to give a simple definition of the concept of remote computing. Remote computing, in essence, refers to the practice of using computing resources that are not physically present at the user’s location.

The simplicity of this definition hides the complexity of the subject. For example, remote workers who require access to corporate systems will require entirely different resources from an Internet of Things (IoT) device that needs to process data in real time. This is where the major differences between cloud and edge computing come into play.

Cloud computing is best suited to scenarios that process large amounts of data. Conversely, edge computing is more suited to processing less data, but in real time.

This is a simplified description of the difference between the two remote computing models. Let’s break it down a bit by examining some of the metrics that help define cloud and edge computing:

These differences define the benefits of each model and determine their use cases.

Edge and Cloud Computing in action

The unique features of each model are what make them suitable for different use cases. Understanding the scenarios in which each model excels is the easiest way to understand the difference between the two approaches to remote computing.

There are gray areas where the two methodologies collide. But, in general, they provide distinctly different services.

Cloud computing use cases

There are many benefits to cloud computing. It is mainly used in situations where large amounts of data are stored, accessed and managed from a centralized location. Among the scenarios in which these attributes make this choice correct are:

• Data analysis: The era of big data is upon us, and organizations often rely on cloud computing to analyze massive data sets.
• Remote work: Cloud-based services are a critical component of the shift to remote and hybrid work. The cloud allows workers to access work resources from anywhere with an internet connection. This could be in the form of basic access to work files or it could be in the form of remote access to work computers and remote apps.
• Software as a service (Saas): The rise of the SaaS model of buying and using software is largely facilitated by cloud computing.
• Disaster Recovery and Backup: Cloud systems are often used as backup and disaster recovery solutions. One example that most people are aware of is pictures stored on your phone. These are backed up on a cloud-based system which ensures their safety in case your phone is lost or replaced.

The common thread running through these uses is the need to manage and process large amounts of data. While this can happen in real time, this is not a core feature of cloud computing.

Edge computing use cases

Edge computing is best suited to real-time processing of small amounts of data. It is aimed at scenarios where latency needs to be minimized and immediate actions are required.

Common uses for edge computing include:

• Internet of Things (IoT): IoT devices are becoming more and more common. Everything from smart homes to smart cities depends on IoT devices. In turn, these often require real-time data processing and edge computing provides this.
• Game: Every gamer has at one time or another experienced the frustration of lag in the game. Edge computing with its low latency, “edge” computing and real-time data processing makes it the perfect choice to ease the frustration of lag. A great example of where edge computing comes into play in games is in games like Pokemon Go, where real-time player data is an integral part of the game.
• Streaming content: This is another field where edge computing is used to alleviate buffering and lag issues.
• Augmented and virtual reality: Applications using augmented or virtual reality require access to real-time data processing to deliver seamless immersive experiences.

Edge computing is the preferred solution where low latency data access is required.

The future of cloud and edge computing

Predicting the precise future of these is difficult. The rapid spread of remote working practices, the IoT and artificial intelligence will all play a key role in determining the future of these forms of remote computing.

However, these offer some clues as to how we can expect them to evolve. There are three main aspects to consider when it comes to the future:

• Cloud Computing: As more organizations move to more remote working practices and take advantage of ‘big data’, cloud computing will continue to grow.
• Edge Computing: The rise of the IoT and the need for real-time data processing are driving the growth of edge computing. As more and more devices become internet-enabled and generate data, the need for edge computing to process this data quickly and efficiently will only increase.
• Hybrid models: Eventually, the lines between these technologies will blur, and hybrid models that can take advantage of both will likely become more prevalent.

Portraying the future is always a hit and miss affair. However, there is no doubt that both of these technologies will continue to develop rapidly.

Head in the Clouds or Life on the Edge

The rise of remote computing in all its forms means these technologies are here for the long haul. Both cloud and edge computing have strengths and weaknesses that largely determine the scenarios that employ them.

However, the future probably lies in hybrid models that combine the strengths of both models. These networks will combine the scalability and data processing capabilities of cloud computing with the real-time, low-latency processing capabilities of edge computing.