The rise in cloud traffic that is expected as a result of increasing applications of the Internet of Things (IoT) might render cloud computing unmanageable. IoT hinges on processing device generated data and cloud computing involves using data from centralized computing and storage. Thus, this computational model can become overwhelmed if the growth trajectory of IoT continues as it has been.
The unprecedented amount of data generated by IoT devices is putting considerable strain on the internet architecture. Consequently, developers are finding ways to alleviate this network pressure and get around the data problem.
What is edge computing?
One of the proposed solutions to this issue is edge computing. This data processing archetype involves pushing data handling to the edge of the network, closer to the source of the data. In other words, instead of sending data to the cloud server or central data center for processing, the device connects through a local gateway device. This allows faster analytics and reduces network pressure.
There are uses for both these types of computing models, but they are inherently different and suited to different niches. Applications for industrial IoT technology where instantaneous decision making is essential is better matched to edge computing, whereas cloud computing is appropriate for big data analytics.
Achieving a stable and sustainable network depends on the balancing act between processing on the edge and the centralized system. Edge computing is generally for custom-built systems, and cloud computing is a more universal platform that is usually more compatible with third-party and older applications. The industry is not looking to replace one with the other, but use them in their best use roles to complement each other and the devices they power.
When IoT needs the edge
For IoT and highly distributed applications, the infrastructure comprises the device, network edge and server. The objective is to process near the device, for instantaneous response and subsequent decision making. This is especially true for applications using generated data in algorithms that use machine learning to make autonomous decisions. Sending the data back to the central cloud server could negate the anticipated value. This approach leverages resources that may not have continuous network access, for example tablets and smartphones in an agricultural setting.
For example, an industrial application of IoT may include sensors in the manufacturing production line or smart traffic lights. Edge devices capture real-time information that can be used by the devices themselves to internally process the data to prevent a part from failing, reroute traffic or even optimize production.
When cloud works best
Cloud computing is still an important processing paradigm and is useful in applications that are not as sensitive to a timeout response, where the device does not need processing power itself or big data applications. This computing model serves to increase the efficiency of everyday tasks and provides a pathway for the massive amounts of data to travel to its intended endpoint.
Edge computing has benefits for applications where the device has data processing capabilities and needs to quickly process data in response to manufacturing parameters that may or may not be within limits. That said, inventory control information is not likely to utilize edge computing. By processing these transactions at the edge of the network would result in a distributed, unsafe and uncontrollable disarray of data.
Towards a balanced strategy
As mentioned, edge computing does not replace cloud computing. Effectively, the analytic algorithm may be fashioned in the cloud and then pushed to the edge device. This is often occurs where the device is primarily a sensor gathering data and incapable of analysis.
The trick is to incorporate both models to their best effectiveness: edge computing where time is of the essence, and cloud computing where security and volumes abide. It is imperative that IoT strategies integrate stacks and layering of the computing exemplars to get the best of both worlds and optimize the processing power of IoT.