Advantages and Disadvantages of Integrating Edge Computing into a Cloud Architecture – Analytics Insight

Advantages and Disadvantages of Integrating Edge Computing into a Cloud Architecture

Edge Computing

Edge computing is a distributed computing technology in which intelligence is integrated into edge devices, also known as edge nodes, enabling data to be processed and analysed in real-time near the data collections source. Data does not require to be uploaded to the cloud or a centralised data processing system when using edge computing.

Why Use Edge Computing?

Most businesses store, manage and analyse data in a centralised storage system, which is usually a public cloud or a private cloud. Standard infrastructure and cloud computation, on the other hand, are no more able to satisfy the needs of many real-world applications. In the context of the Internet of Things (IoT) and the Internet of Everything (IoE), for instance, a highly flexible network with low latency is necessary to handle massive volumes of data in real-time, which is not achievable on standard IT infrastructure. The benefits of edge computing become more apparent in this situation.

Edge Computing Advantage

Edge computing eliminates the need to transport data to the cloud for processing and analysis since data is processed near the data accumulation point. This method reduces the strain on both the network and the servers.

Edge computing is extremely useful in the field of IoT, primarily industrial IoT, due to its capacity to handle data in real-time and its quick reaction time. Edge computing technology provides for additional breakthroughs, such as AI and MI, in addition to faster digitalization for industrial and manufacturing businesses.

Edge Computing Limitations

Consider if it makes sense to support certain edge models before moving a task to the edge. These constraints may force you to return to a traditional cloud design.

Security on the Edge

By reducing the amount of time data spends in transhipment, edge computing minimises some security concerns, but it also adds more complicated security issues.

If you host or process the data on end machines you don’t manage, for example, it’s impossible to ensure that such devices are free of flaws that attackers may exploit. Even if you adopt a cloud-edge architecture that gives you command over the edge infrastructure, having the additional infrastructure to maintain expands your attack surface.

Edge computing is therefore unsuitable for applications with stringent security requirements. If you’re working with sensitive data or have particular compliance needs, a typical cloud computing approach with centralised servers may be less hazardous.

Latency Requirements

Because data does not have to travel back and forth between cloud data centres to be analyzed, edge computing enhances application speed and responsiveness. For tasks that need really instantaneous communication streams, this is a significant benefit. Cloud companies continue to expand their data centre footprints, but these huge facilities are frequently located in rural areas far from major population centres.

The majority of workloads have lesser latency requirements. An edge network may only enhance network responsiveness by a few milliseconds when compared to a typical cloud design. The inefficiencies that come with traditional designs are tolerable for ordinary applications.

Check to see if the latency benefits are actually worth the compromises, especially when you consider the additional expense and administration load.

Data Volume

Determine how much data your operations will handle and if your edge infrastructure is capable of handling it. If your job creates enormous amounts of data, you’ll want a large infrastructure to evaluate and store it. Moving the data to a public cloud data centre is likely to be less expensive and easier from a management standpoint.

Workloads that are essentially stateless and don’t require significant amounts of data, however, are ideal candidates for edge computing.

Edge Computing Examples

Here are some examples of when edge computing is and isn’t a suitable match to demonstrate the aforesaid trade-offs. The following are some good examples of edge computing:

Autonomous Vehicles

Self-driving cars acquire a lot of data and have to make quick choices in order to keep passengers and others on the route safe. Latency problems might result in nanosecond delays in vehicle reaction times, which could have serious consequences.

Smart Thermostats

These gadgets produce a little amount of data. Furthermore, some of the information they gather, such as when individuals get home and adjust the thermostat, may have privacy issues. It’s more practical to keep data at the edge, and it can help with security problems.

Traffic Lights

Three features of a traffic light make it a suitable candidate for edge computing: The requirement to react to changes in real-time; limited data production; and internet connectivity outages on occasion.

Here are a few scenarios in which edge computing does not function well:

  • Conventional applications: It’s difficult to imagine a traditional application that demands edge infrastructure’s performance or reactivity. It may reduce the time it takes for an app to load or reply to queries by a few milliseconds, but the benefit is rarely worth the money.
  • Video camera systems: Videos produce a large amount of data. It’s not practicable to process and store such data at the edge since it would need a huge and specialised infrastructure. Storing the data in a centralised cloud facility would be considerably less expensive and easier.
  • Smart lighting systems: Lighting control systems that work over the internet in a house or business don’t create a lot of data. However, light bulbs, even smart ones, have limited processing power. Lighting systems don’t have ultra-low delay requirements, so it’s probably not a huge problem if your lights take a fraction of a second to switch on. You could create edge infrastructure to manage these systems, but in most cases, it’s not worth the money.

Conclusion

By outsourcing processing to end clients’ devices, edge computing can minimise transmission delay, lower data access to the network, and, in certain circumstances, cut expenses. Because of the benefits, cloud architects may seek to move as many workloads as possible to the edge. However, before they do so, they should think about the structure of each application, its performance needs, and security concerns, among other things.

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