Epsilon chooses Dialogic SBC to enhance global interconnect fabric

Epsilon Telecommunications has selected Dialogic's BorderNet session border controller (SBC) software to grow its global interconnect and SIP trunk business and improve time to service through virtual SBC automation.

Epsilon is upgrading its global interconnection network within its core network, introducing the flexibility to distribute session control, routing, network visualisation, and real-time voice quality measurement (VQM) to the network edge.

"We chose the BorderNet SBC because of its reduced capex and opex profile and because it enables us to accelerate time to revenue through virtualisation and automation by integrating with our on-demand platform called Infiny. The integrated Dialogic Analytics system was another key business driver to select the Dialogic solution," said Vibeke Harder, director of operations and engineering at Epsilon. The company launched the Infiny On-Demand connectivity platform at Capacity Middle East in March. 

The virtual single-software BorderNet SBC was selected to reduce opex associated with its globally distributed network growth by enabling rapid SBC deployment and automated provisioning through REST APIs. 

Epsilon is reducing capex due to BorderNet SBC network-based licensing, which provides a global session licence pool that is shared across all SBCs, eliminating unused licences on individual SBCs.

Jim Machi, SVP of product management and marketing at Dialogic, added: "Dialogic continues to enhance the BorderNet SBC features and operational characteristics to enable service providers and enterprises to confidently begin the transition from appliance-based SBCs to virtual and cloud deployment models, including high availability public cloud deployment in the Amazon Elastic Compute Cloud (Amazon EC2). The Dialogic single-software BorderNet SBC supports a wide range of service provider and enterprise deployment options ranging from 25 sessions to 100,000 sessions per SBC instance depending on the deployment model.”