ConteXtream and Guavus to make service provider networks programmable with real-time analytics

SINGAPORE: ConteXtream Inc., a leading provider of carrier-grade network virtualization solutions, has partnered with Guavus, a leading provider of big data analytics solutions, to bring real-time analytics to Software Defined Networking (SDN) deployments on service provider networks.

The partnership will combine ConteXtream's ContexNet, which collects detailed analytic records on web traffic and makes this data available for analysis, with Guavus' Reflex analytics platform, giving network operators an end-to-end view across their network enabling them to make better quality decisions more quickly.

ConteXtream's ContexNet is a carrier-grade distributed SDN fabric that leverages proven virtualization and grid computing technologies to enable operators to accelerate revenue growth through more rapid testing and introduction of new services or features; utilize local and remote network resources on demand, and reduce CAPEX and OPEX by automating network operations, ContexNet runs on standard, off-the-shelf computing platforms, creating a single, distributed and scalable SDN domain, dynamically links network functions by steering the right traffic flows to virtual functions in the right sequence.

ContexNet is compliant with industry standard-based mechanisms such as OpenFlow, LISP and OpenStack which enable it to fully separate control from forwarding, location from identity, and orchestration from networking.

Guavus' Reflex Platform provides a real time integrated view across customers' business and operations for improved decision making. As a result, carriers benefit from an end-to-end view across their enterprise with a highly granular resolution and actionable insights that can be embedded into automated workflows to rapidly implement value added services and develop network policies that effectively reduce capital and operational costs.

Reflex uses highly optimized computational algorithms and machine learning to distill actionable insights from very large datasets. This allows for continual optimization of the computational process, as well as enables improved forward-looking and predictive analysis, real-time decision-making and automation.