The processing power at the infrastructure edge is what makes the low latency and high-performance preconditions needed to make Machine Learning (ML) and Artificial Intelligence (AI) applications work. ML and AI applications are often located at the edge and sends large amounts of processed data from devices to a data center, with high bandwidth and low latency as a common prerequisite.
High-frequency data collected by sensors is more and more often processed with ML and AI with extremely low latency generating immediate insights that can initiate needed actions. The network load is thoroughly reduced as the raw data is not sent — only needed and often enriched data is instead being sent to the cloud for further analysis. Connected devices are becoming less dependent on high-quality network connections. However, even if the vast amounts of raw data is transformed or condensed into insights, the amount of data will become more and more demanding as the number of data collection points grows in an enterprise. This calls for the utilization of smart technology such as the CloudBackend Edge dbPaaS — A smart data management platform designed to make scaling of ML and AI utilization effective and industrialized. In order to train ML and AI models there is an increasing need of processing power. This is predicted to continue for years and demand more localized processing power, data storage, and resources such as data management platforms.
Distributed data management and orchestration of data and computing across edge infrastructures, on premises, and central clouds is required — This is where CloudBackend's Edge dbPaaS provides a universal solution in which data is automatically synchronized between a micro cloud at the edge and other clouds (global/regional/edge/micro clouds), according to configuration. The distributed cloud architecture includes solutions for user accounts, limiting access to data, anonymization and more.
With a micro cloud from CloudBackend integrated in the edge node, innovative functionality is enabled, such as user experience that follows the user between different mobile devices. It also simplifies data synchronization to/from infrastructure, mobile devices or other edge end-points. On the infrastructure side it simplifies distribution of data (enabling or restricting) between geographical areas or parties, and reduces the need of data transfers. The CloudBackend data management API:s are served locally by the micro cloud in the edge node and functions are executed there or in another cloud higher in the topology and acting as the data layer for ML and AI applications processing the data.