Cognitive Computing Technology to Revolutionize Intelligent Decision Making in 5G management software
The configuration of a next generation 5G network is a very difficult task for the telecommunications operator or the Mobile Virtual Network Operators making use of the underlying telecom substrate. Many customer services require guarantees which should never be affected by the state of the substrate network and computing resources. Indeed, the commercial potential of many promising 5G applications will only come to life if the network itself is capable of automatically deciding and enforcing at runtime the most suitable resource allocations and configurations.
Modio delivers an innovative solution for operators and IT software vendors for telecommunication SDN/NFV cloud networks.
In contract to existing machine learning platforms, our Value Proposition is formed around our theoretical and software programming expertise to implement custom artificial intelligent techniques and to integrate them with next generation management IT solutions for telco clouds. This derives from our knowledge drawn from the intersection of machine learning and 5G concepts and technologies. Our solution is customizable to be adopted to help solving real problems arising in a certain range of 5G use cases.
Core of our machine learning engine is the Qiqbus streaming analytics platform, an in house developed Spark based platform (see section Qiqbus) which Modio has installed in commercial projects of for mobile advertising, Deep Packet Inspection and other commercial projects demanded by our industrial customers.
Qiqbus high level Architecture & Data Flow
The platform has been designed to operate on a cloud environment. It implements a share-nothing architecture that allows it to scale to a very large number of users, input streams and traffic rate. It is geographically distributed, with each geographic zone operating in complete isolation from the other zones. The platform is fully multitenant with each tenant managing her own set of users, devices, processing topologies and alerts. The supports features such as Cloud-to-Device messaging, multimodal streaming analytics and real-time dashboards and in-memory analytics engine for mission-critical applications.
Our codebase currently supports implementations of machine-learning models, incl. neural networks, decision trees, support vector machines, regression analysis & bayesian networks.
For more information on our 5G management technology contact us at email@example.com
Part of our work on 5G management, specifically our work for autoscaling of NVF resources using predictive analytics has been funded by the following European Commission H2020 projects
SOFTFIRE – GA 687760
FED4FIRE+ – GA 732638
5GINFIRE – GA 7324971
Our ongoing work on a Mobile Edge Computing decision-making engine for MPEG-DASH is supported by the H2020 FLAME project
Modio participates to Open Source Mano Working Group, focusing on providing to OSM with predictive-analytics based autoscaling features