Edge is gaining momentum
With the the advance made on RISC architecture (Reduced Instruction Set Computer) reducing latency and improving further power efficiency, edge is gaining momentum. A shift driven also by advances made in machine learning (tinyML) and AI applications (RL) as well as autonomous vehicles, VR/AR, 5G and IoTs
The IoTs are spreading rapidly with the expansion of cloud-to-edge new range of machine learning and artificial intelligence (ML/AI) chips that are built-in to accommodate high-performance computing (HPC) for processing streaming data. ML and AI at the edges include deep learning (DL) and reinforcing learning (RL) to process data flow and carry out analytics on real-time. This change in the analytics landscape is creating a new analytics interface supported by new cloud-to-edge architecture.
The need to carry out real-time analytics at the edge of networks is driven by the rapid proliferation of sensors and also the increase of machine to machine communication, and connectivity, including the new satellite wireless connectivity. The IoT connectivity services are on the rise and are currently used to provide wireless connectivity and expand IoT services to remote areas.
Gartner predicted that this year (2022), 75% of enterprise-generated data will be created and processed outside a centralized data center, “lake” or cloud. DataAge 2025 study also reported that by 2025, 20 percent of all data created will be real-time in nature that would no longer require sending it to the core of the network, to the cloud as raw data for processing. According to Gartner edge computing and analytics capabilities will be further enhanced by new AI tiny chips, 5G technologies and advanced processing.
An interesting example where edge analytics was used is the double-engine boat “SilverHook” with a speed of 200 miles/hour. The data generated was received and instantly analyzed to assess the boat’s performance during the race. An algorithm generates data to be returned to the crew in seconds. The challenge is that the double powerful engine must operate at its highest level without overheating while increasing the chances to beat world records. The real-time feedback allows the release of pressure just at the right time based on real-time analytics embedded in the boat navigation system.
In this changing analytics landscape, there is a shift from cloud analytics to edge analytics to address latency as well as security issues. With an unprecedented growth of already voluminous data, the time that it will take for data to transit from a IoT device to “negotiate” a complex network topology at the cloud, it could be too lengthy for effective device management at the edges. By the time data reaches the cloud for analysis, the opportunity to act on it may be missed, especially in remote locations.
For data to travel from the edge, where it is generated to a cloud provider and back, it may take between 150 to 200 milliseconds, while at the edges it may only take between 2 and 5 milliseconds for data to transit back-and-forth locally. The implications can be enormous, when dealing of image recognition or signal processing to assess the quality of products and improve overall productivity and operational processes at the edge.
