Evaluating Aviz Service Node capabilities using the Spirent Landslide ability
Aviz Service Nodes (ASN) improve network observability through general-purpose hardware, offering cost-effective performance enhancements. Their capabilities provide Subscriber intelligence by metadata extraction and correlation across various network types, including 4G-LTE and both 5G architectures.
Technology Features
Autonomous System Number (ASN) supports 5G-SA, 5G-NSA, and 4G-LTE systems to facilitate metadata extraction and ensure alignment between User and Control Plane data. This process is vital for accurate data management, analysis, and decision-making.
The company stated, “The comprehensive metadata extraction and correlation capabilities of ASN are designed to provide unparalleled network insights, ensuring our clients can make informed decisions quickly and accurately.”
Handover Management
During network handovers, ASN manages the seamless transition of ongoing calls or data sessions. It effectively extracts and correlates necessary control and user packets throughout this process, accommodating various handover scenarios in modern networks.
Application Identification
Identifying applications within telecom traffic is essential for tasks such as traffic management and security. ASN uses Deep Packet Inspection (DPI) to classify applications based on metadata elements like Server Name Indication and application behavior.
Performance Metrics
ASN is equipped to handle up to three million subscribers and can manage user traffic up to 150 Gbps. Validation tests confirm its performance under realistic traffic conditions, emphasizing its ability to function effectively in large scale telecom environments.
The testing utilized Landslide to simulate 5G network conditions, which supported the validation of ASN's capabilities in handling complex scenarios.
Conclusion
This summary reflects the capabilities of Aviz Service Nodes, emphasizing their role in enhancing network observability through effective metadata management. ASN's features align with industry demands for improved data analysis and network efficiency.