How to Mix Edge Computing and Cloud Computing Architecture

As far as computing within industrial ecosystems is concerned, enterprises deploy one of the three available options, or a mix of them. These computing options are on-premise IT architecture, cloud computing, and on-premise cloud ecosystems. Although the location of data centers differentiates these three options, they handle data analytics and computing from a centralized data center and network. Edge computing, on the other hand, makes use of small, portable, decentralized data centers and servers which reduces the distance between the data producing point and processing points.

This article will discuss:

• Why edge computing is the future of industrial data processing
• How uniting edge computing and cloud computing drives automation
• The benefits of mixing both edge computing and cloud computing

The need for a new computing concept

Exponential increases in workloads have been known to overwhelm IT infrastructures, including both on-premise and cloud architectures. In 2018, Black Friday flash sales downed J. Crews on-premise data centers, while in September 2019, cloud powerhouse, Amazon, experienced downtime that affected thousands of its clients. Both examples show that even the most reliable computing networks can experience downtime, which calls for a better way to handle sensitive computing needs.

Alongside increasing workflows, the need to achieve real-time data transfers to automate industrial processes highlights the need for new computing concepts. This is because high latency and low bandwidth cause delays in round-trip timing. Here, round-trip timing refers to the time it takes for the data produced from a piece of equipment to get to the cloud and back to the device. The delays during the data transfer process hamper real-time analytics and can lead to downtime.

Lastly, with approximately 85 million IoT and IIoT devices across the globe, the competition for computing resources is increasing. This increase can stretch both on-premise and cloud networks to the limit, which can lead to downtime and data transfer delays. These challenges have led to emerging technology solutions such as edge computing and 5G networks. While 5G networks intend to deal with the challenges of high latency and low bandwidth, edge computing is the new computing concept the industrial community has been waiting for.

Within this computing concept, is the simultaneous application of both edge computing and cloud computing to enhance industrial operations. In its simplest form, the concept involves the use of edge hardware within shop floors and cloud computing for more centralized applications. ,

Uniting the best of both worlds

The unification of edge computing and the cloud delivers what the industrial community needs: rapid response times and big data processing. In this model, edge computing provides the rapid response or increased data processing speed needed to deliver real-time solutions. Also, the cloud’s universal platform is ideal for managing big data and provides a framework for developing new applications. Unifying both computing options supports the delivery of the following features:

• Enhanced innovation opportunities – The cloud provides platforms that support the creation of analytic algorithms and applications for edge computing. With the expanded resources of the cloud, custom container applications built for microprocessors and mini operating systems can be developed. These applications or algorithms can then be tested before deployment into edge hardware which equips these devices with analytical abilities.

• Time-sensitive applications – To achieve industrial automation, hacking real-time analytics across the shop floor is an important step to take. Although edge computing alone is capable of managing time-sensitive applications, the integration of cloud computing enhances the process. In an IIoT-driven environment, edge hardware can handle edge computing challenges while sending complementary data to the cloud. The large computing resources within the cloud can then be used to handle scheduling and simulation analytics for every asset, system, and process within a facility.

• Enhanced storage options – With tens of IIoT devices, legacy assets, and industrial processes producing data, there is a need to ensure only relevant data is captured. Edge computing provides a solution to reducing the data flow within a system by capturing important data and discarding temporary information. Facility managers can then choose to either transfer data from edge devices to the cloud or discard them. When properly done, this data management process reduces the amount of money spent on cloud resources and other storage options, thus reducing the total cost of owning cloud-based solutions.

• Enhancing security – The increasing cybersecurity challenges enterprises face has led to warnings about plugging IIoT loopholes. This is because hackers continuously employ tools to detect holes within enterprise networks to either steal sensitive data or disrupt workflow. The ability of edge computing to handle data processing at the device level ensures there are fewer network pathways that can be explored by hackers. The option of deploying security information and event management (SIEM) tools across the cloud also provide blanket protection to the data IIoT devices transfer back to it. While the decentralized network reduces vulnerabilities, threat intelligence can be deployed in the cloud.

The benefits of mixing edge computing and the cloud

The application examples discussed above highlight the more important benefits of a symbiotic relationship between edge and cloud computing. Other benefits of the simultaneous use of both technological solutions include:

• Offline computing – Edge computing devices are equipped with the data processing resources needed to deliver offline computing. Thus centralized downtime or communication lags do not affect processing and the edge hardware can reconnect back to the cloud once the network is up.

• Enhanced user engagement – The combination of edge computing and cloud computing also enhances the integration of augmented reality within physical environments. The data collected from both options can be used to map experiences, train employees, and simulate problem-scenarios.

Conclusion

As IIoT technology becomes pervasive within industrial facilities, the need for supporting computing options will continue to increase. This is where edge computing and the hardware that drives its integration across shop floors come into play. Thus, to achieve real-time automation as well as capture and process data from legacy equipment, the symbiotic relationship between edge and cloud computing must be nurtured. Accomplishing this will speed up the adoption rate of cloud computing in traditional facilities and the integration of Industrie 4.0.