ARUBA CX 8320 SWITCH SERIES - 2GHZ CPU, 16GB RAM, 64GB SSD, AND 8GB FLASH - (48)

• 8320 Layer 3 Switch
• 5 x Fans
• 2 x Power Supplies
• 1 x 2-Post Rack Kit

The past several decades in networking have been defined by static, closed networking solutions designed for the clientserver era. Aruba is introducing the Aruba 8320 campus core and aggregation switch series, a game-changing solution offering a flexible and innovative approach to dealing with the demands of the mobile, cloud and IoT era.

The 8320 switch series provides industry-leading line rate 10GbE (SFP/SFP+ and 10GBASE-T) and 40GbE connectivity in a compact 1U form factor. Together with the modular Aruba 8400 chassis, the 8320 rounds out Aruba's Mobile First switching portfolio with an enterprise core and aggregation solution that ensures higher performance and higher uptime.

The 8320 switch series 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 zeroservice disruption during planned or unplanned controlplane events. The key innovations in ArubaOS-CX are its micro-services style modular architecture, REST APIs, Python scripting capabilities and the Aruba Network Analytics Engine.

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.

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.