What CIOs need to know about Edge & Cloud - Internet Of Things

1. What CIOs need to know about Edge & Cloud The Fourth Industrial Revolution has brought the Internet of Things (IoT) to the frontline of manufacturing and technological advancement. Termed as “Industrial Internet of Things (IIoT)” in this context, the system allows industrial microcosms to communicate and function as a whole via the Internet. Using the IIoT, companies can now automate their entire business process, from manufacturing to logistics, to enhance productivity and efficiency, and reduce expenditure across several verticals. Some of the most popular technologies that constitute the IIoT are Cloud Computing, Machine Learning, Artificial Intelligence, Deep Learning, and Smart Manufacturing. Companies use Cloud, a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer, to add the IoT functionality to their industrial systems. IIoT devices generate a massive amount of data each day, which has resulted in the rise in Cloud traffic and increased pressure on the Internet architecture. This unintended consequence may make Cloud servers unmanageable in the future. Developers are figuring out ways to create a better alternative to automate and connect the industry. One such way or paradigm is Edge Computing. It is important to note that the alternate is not a replacement for Cloud as a whole. Edge and Cloud are designed for different niches, but the latter is being used more prominently due to the lack of a viable alternative, until now. Edge is designed for handling the instantaneous decision making IIoT needs, whereas Cloud is more suited for Big Data analytics and other high-volume applications. Edge can also be seen as a way to optimise Cloud computing systems. Difference between Edge & Cloud Edge Computing is a distributed Information Technology (IT) architecture in which data is processed at the periphery of the network, close to the source of origin. In other words, instead of sending data to the Cloud server for processing, the Edge device does it through a local gateway device. This results in faster analytics and reduced network pressure. For IoT, the goal is to process near the device for immediate response and subsequent decision making. And the goal becomes even more pronounced for applications using generated data in algorithms that use machine learning to make autonomous decisions. This is where Edge comes

2. to the fore. Edge processes data at the near the device and retrieves responses quickly, and is especially useful in applications that are sensitive to a timeout response. Edge is useful especially for applications where network access is limited or irregular. For instance, a manufacturing production line uses the IoT. Edge devices will capture real-time information about the production process and process it internally to optimise production. Cloud Computing is useful in applications that aren’t as sensitive to a timeout response or applications that don’t require processing power. The computing model increases the efficiency of everyday tasks and provides a pathway for the massive amounts of data to travel. Cloud allows for large amounts of data to be stored and processed. Edge Computing is more useful in tasks where the Edge device already has data processing capabilities and only needs to quickly process smaller amounts of data in response to manufacturing parameters whose limits may or may not be defined. However, Edge should not be used for inventory control information as processing it at the periphery of the network would result in an unsafe and uncontrollable disarray of data. Balancing Cloud on the Edge While deciding what system to use for automating their manufacturing or industrial unit, CIOs need to strike a balance between Cloud and Edge. Although Edge isn’t a replacement for Cloud, using them interchangeably in an interconnected environment can pose security hazards and cause the lowering of productivity. To use the two systems synergistically, the analytical algorithm may be produced via Cloud Computing, and then pushed to the Edge device for processing and analysing in short bursts. This is especially useful when the device itself is incapable of performing analysis. To paraphrase, use Edge where time is important, and Cloud where data security and volume are. To make the most of their interconnected and automated industrial ecosystems and optimise the processing power of their IoT systems, it is recommended that CIOs use layers of strategically stacked Edge and Cloud systems.