Buy or Sell HPE 8400 JL376A Chassis

The past several decades in networking have been defined by static, closed networking solutions designed for the clientserver era. Aruba is introducing the Aruba 8400 campus core and aggregation switch, a game-changing solution offering a flexible and innovative approach to dealing with the new application, security and scalability demands of the mobilecloud and IoT era.

The 8400 provides industry-leading line rate 10GbE/40GbE/100GbE connectivity in a compact 8 slot chassis. Together with the compact 1U Aruba 8320 Switch Series, the 8400 rounds out Aruba’s Mobile First switching portfolio with an enterprise core and aggregation solution that ensures higher performance and higher uptime.

The 8400 is based on the new ArubaOS-CX, a modern software system for the enterprise core that automates and simplifies many critical and complex network tasks, delivers enhanced fault tolerance and facilitates zero-service disruption during planned or unplanned control-plane events. The key innovations in ArubaOS-CX are its micro-services style modular architecture, REST APIs, Python scripting capabilities, Aruba Network Analytics Engine and Aruba Virtual Switching Extension (VSX).

ArubaOS-CX is based on a modular architecture that allows individual process re-startability and upgrades. Its REST APIs and Python scripting enables fine-grained programmability of the switch functions and its unique Aruba Network Analytics Engine provides the ability to monitor and troubleshoot the network easily.

Aruba’s new virtualization technology, Aruba VSX, takes advantage of the ArubaOS-CX modern architecture, and delivers best in class high availability required by campus core and aggregation solutions.

The Network Analytics Engine framework is made up of a time series database and associated REST APIs.

The time series database may be used to store configuration and operational state. Customers can use ArubaOS-CX REST APIs, Python scripting capabilities and time series data to write software modules for trouble shooting problems. The time series data may also be used to analyze trends, identify anomalies and predict future capacity requirements.