Edge computing, edge cloud and cloud computing: differences and opportunities

5 min

Data is the basis for successful analyses, processes, and strategic decisions. Numbers, measurements, and statistics are the basic language of digital organizations. Now we need to understand where to get the data from and where to process and store it in order to get the right support at the right time. This is where cloud computing, edge cloud, and edge computing come in. Let’s analyze together what these terms mean.

Edge computing: the benefits of proximity

The proliferation of data is closely linked to the exponential growth of connected devices thanks to the Internet of Things (IoT). Sensors attached to devices and connected to the network have made it possible to collect information in a continuous loop. This is raw data, which has no value in itself unless it is incorporated into a process of processing, storage, and analysis. Not only that, but it is heterogeneous and, depending on its function can be used in real-time or collected for future processing. Therefore, depending on location, latency, power, and storage capacity, we will have different solutions to consider.

Edge computing allows us to bring computing capacity closer to the source of data generation by leveraging current networks and Internet of Things (IoT) technologies. The organization that chooses this solution can therefore process data close to the device without sending it to the IT infrastructure of a traditional data center or to the cloud for further analysis. Edge computing is a decentralized system that offers low latency, as the data is processed on-premise. The cons, in this case, relate to processing and network limitations, as well as potential privacy issues. In fact, the issue of cybersecurity, as it relates to data management, also affects edge cloud and cloud computing.

Edge computing examples

What could be the most suitable use cases for edge computing? Scenarios in which one needs to act in real-time and therefore require low latency and high responsiveness. One example is self-driving cars. If the car has to stop when a pedestrian passes by, the data that detect the pedestrian’s presence must be processed quickly to trigger an immediate response. The same applies to medical monitoring, where the IoT device, which detects a patient’s vital parameters, will have to extract information as quickly as possible and process it locally to respond appropriately to detected emergencies.

Edge cloud computing

Edge cloud: when interaction improves data management

Before defining the edge cloud, it may be useful to imagine a technology stack divided into three layers to understand how these technologies interact. In the first layer, we find connected IoT devices that collect data and process it locally through the edge computing paradigm. The result is reduced latency and optimized efficiency. In the second layer, called the ‘edge cloud’ – but some experts call it ‘fog‘ if no external cloud provider is used – the data collected by IoT devices is transformed into more meaningful information. Processing takes place on local computing devices, or edge cloud servers, located a short distance from the data collection devices. This layer provides more processing power than IoT devices and is activated for more complex information. In the final step, data requiring advanced data analysis tools are sent to the cloud, where we can take advantage of scalable, high-level computing and storage resources.

After this brief digression, we return to the definition of the middle layer, the edge cloud: an extension of the cloud computing paradigm to the edge of the network. In this case, servers, storage, and the network are distributed locally to process and store data in close proximity to the devices that generate it. At the same time, however, the infrastructure also relies on the cloud for more complex tasks involving multiple resources. It is, therefore, a hybrid approach that takes advantage of both centralized cloud resources and localized edge processing. In this way, low-latency data and bandwidth can be flexibly allocated to the tasks that need it at any given time.

Edge Cloud examples

The Edge Cloud is particularly suited to large industrial environments where production and management activities run in parallel. Devices and edge gateways will collect data from sensors and machines on the factory floor, performing real-time analysis and monitoring. They will then send more complex information to the cloud for deep processing for strategic and decision-making activities. The edge-cloud architecture helps improve operational efficiency, reduce downtime, and enable proactive maintenance.

Understanding the characteristics of edge computing, edge cloud, and cloud computing helps organizations choose the best solution for data processing, storage, and analytics. Click To Tweet

Cloud computing: centralizing data to support decision making

Here we are at the final level of the technology stack, where information is needed to initiate data-driven decisions and support management. We are at the stage where we can move away from the immediacy of data in favor of mature, pre-processed information that will guide short and long-term decisions. At this level, we need centralized data management and increased processing and computing power.

To meet these needs, we rely on cloud computing, which is the use of online computing resources – servers, storage, databases, software applications, and online networks. We can choose to pay a subscription to use the infrastructure created and shared by cloud service providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, using their virtual resources. Or we can build our own cloud computing infrastructure.

What are the benefits of managing and processing data using external infrastructure? First of all, scalability and elasticity. As a customer, we can scale resources up or down as needed, optimizing utilization and cost efficiency. In addition, if we do not have an in-house data security and privacy department, we can rely on the security systems of, for example, Amazon, Google, and Microsoft. Finally, while implementing an in-house IT infrastructure requires an investment based on projections (which we may even overestimate), by relying on cloud services, we can only pay for what we actually use.

Examples of cloud computing

Businesses that could benefit from using cloud computing exclusively are, for example, SMEs and start-ups. In small and medium-sized organizations, the management and processing of data does not always justify the cost of implementing an in-house IT infrastructure. Businesses with variable workloads are also well-placed to benefit from the scalability and flexibility of cloud computing. Finally, by relying on online management and storage services, cloud computing can be a viable solution for organizations that need to process large amounts of data. Consider companies that have integrated artificial intelligence or machine learning into their processes. In this case, they benefit not only from the scalability of the services but also from access to specialized resources to run these workloads efficiently.

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How to choose the best solution between Cloud Computing, Edge Computing, and Hybrid Computing?

Any manager or CEO knows that there is no such thing as a one-size-fits-all solution for all companies, even if they operate in the same sector because each organization is a unique organism that requires a tailor-made study. Let’s think about how the human mind works: what would we do if we had to choose between short-term and long-term memory? If we are able to respond immediately to external stimuli, it is because we can retrieve information quickly. In long-term memory, we store information that requires more processing because it needs to be stored for a long time: for example, the information we need to drive a car.

Similarly, each organization will need to find the right balance between managing data close to the source, for those activities that feed on the data in real-time and need to provide immediate answers, and relying on the cloud for data that requires computationally complex processing and deeper, more reflective analysis. There is no one-size-fits-all solution, just as there is no definitive choice between short-term and long-term storage.

For example, I will manage data on-premises to monitor industrial sensors or analyze real-time traffic data to optimize vehicle flows. On the other hand, I will rely on the cloud for machine learning, artificial intelligence, or advanced data analytics, which require high processing capacity and may require longer response times. By balancing these activities, I will be able to assign the right value to the data by unlocking its full potential.

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