Qiqbus

Qiqbus is an enabling platform (iPaaS) that can significantly reduce the cost of development of cloud-native analytics applications for 5G/6G, the Internet-of-Things, Energy and IT privacy. Qiqbus eases the development of analytics applications with a focus on real-time data collection and processing. Users concentrate on building analytics flows while the platform handles such technical complexities as security, privacy, high-availability, scalability, efficient resource scheduling, data visualization, and integration with input and output data streams.

Qiqbus is a general-purpose real-time processing software. It allows receiving events from  through multiple messaging protocols. Events are forwarded to a custom network of distributed processors. A processing network is a collection of filters, transformers and aggregators. Resulting messages are stored to a selection of back-end stores and/or emitted via output APIs. The system is designed for cloud infrastructures, horizontal scalability and full multi-tenancy. The platform has been designed to operate on a cloud-native environment. It implements a share-nothing architecture that allows it to scale to a very large number of slices, subscribers, input streams and traffic rate.

Qiqbus high-level architecture is depicted below.

The ML/AI models database is customisable and extensible. It currently supports implementations of machine-learning models, incl. neural networks, decision trees, support vector machines, regression analysis, Bayesian networks,  and a wide range of Deep Learning frameworks, including CNN, LSTM, RNN and hybrids best fitting specific problems. Moreover, we have implemented an in-house Deep Reinforcement Learning framework catering for resource provisioning problems in Edge telco environments. Qiqbus employs state-of-the-art open source software tools in analytics along with superior large-scale resource management in the cloud and a management interface to offer a coherent software stack for building next-generation big data applications for cloud-native environments.

The platform is ideal as the integration layer for an 5G Edge analytics solution. It’s built-in support for low footprint messaging protocols renders it an ideal candidate for data collection and processing at scale.