Real-time IP communications traffic continues to explode and operators are searching for new ways to increase revenues and profits. With dynamic changes to network traffic, and inefficient network planning, service providers are looking for turnkey solutions to deliver detailed insights of their IP voice networks to maintain the Service level agreements and Quality of service to their customers.
This is where Ribbon’s powerful Network Analytics tool steps in. Network Analytics collects, analyses and interactively displays all available performance metrics, faults, packet and CDR data produced by Ribbon's network elements such as Session Border Controllers (SBCs), Call Controllers and Media Gateways. With a flexible new virtualized and horizontally scalable architecture, Network Analytics enables service providers with drilldown analysis of their network and meet their QoS and SLAs promised to their customers.
Big Data Analytics with Hadoop
Ribbon's Network Analytics provides horizontally scalable data collection and its benefits include:
Storing and Processing Massive Amount of Call Statistics and Performance Data - Adapting big data techniques enables operators to leverage data collection and warehousing to improve network performance and service efficiency, and lower network costs. With improved service quality, the operator can enhance the end user customer experience, leading to improved customer retention and potential new revenue streams.
Scalability - Highly scalable Data Collection combines performance and reliability with flexibility to grow easily as the number of GENBAND’s elements within the service provider’s network grows, with little administration overhead.
Faster Computing - Leveraging the power of Hadoop’s distributed computing model, collected metrics such as performance, fault and CDR data can be processed faster, and larger computing nodes enable higher power for data processing.
Fault Tolerance with High Availability - Network Analytics is carrier grade and designed to protect against failures. The data is stored in Hadoop’s distributed architecture and the application will continue running even when one node in the cluster is offline.