Bridging Workload Orchestration and Network Operations in AI Factories


The transformation of enterprise infrastructure happens in two directions at the same time. In some cases, fast-changing and extremely distributed workloads need to be handled by enterprise infrastructures. On the other hand, enterprise infrastructures need to provide predictability and minimize operational costs.


Managing multi-tenancy throughout the infrastructure and applications is vital in both cases. Also, constant visibility to application and network behaviors is required.


With the increase in workload number and variety, quickly and efficiently resolving customer issues becomes increasingly difficult.


Aviz Networks, together with Red Hat, provides solutions for workload and application lifecycle management with Red Hat OpenShift and Red Hat Enterprise Linux. For management of the network layer, they provide solutions at the Day-0 to Day-2 operations of AI fabrics.


Aviz Service Node (ASN) provides a range of advanced capabilities such as deep packet inspection, packet-level differentiation and metadata enrichment to help identify and extract application information from packet streams. ASN utilizes this information to perform GTP correlation and packet-level deduplication. ASN is designed to provide packet-level insights for a wide range of telecommunications and enterprise use cases.


Network Copilot is an operations support system that provides recommendations and uses artificial intelligence to perform root cause analysis to help communication service providers improve their mean time to repair.


All three solutions (ASN and Network Copilot) are software applications and provide the same functionality across on-premise, edge and cloud environments using Red Hat Enterprise Linux.


Building and operating an AI factory is a challenging endeavor and requires integration and collaboration of various/all platform, infrastructure and operations services. Efforts to decouple and manage these services individually leads to gaps and increasing costs.


Aviz uniquely combines network and AI operations services with Red Hat’s offerings. This provides customers with a way to enhance their AI offerings while improving control and visibility.

Comments

Post a Comment

Popular posts from this blog

Scaling Deep Network Observability for 5G: Reflections from a Real Deployment

Image
  I recently went through a detailed implementation showing how deep network observability enhances 5G mobile user experience while reducing operational complexity. What stood out was how grounded the deployment was in real traffic and production-scale constraints. Some practical observations: • Fragmented systems increase cost and operational overhead • Frequent infrastructure upgrades create instability and repair expenses • Optimised filtering significantly reduces DPI hardware requirements • Real-time KPI computation improves SLA monitoring and service assurance • Standardised data export through streaming pipelines simplifies integration One misconception addressed during the walkthrough was that deeper observability automatically means more hardware. In reality, efficient filtering and intelligent packet processing reduce hardware footprint while improving performance. My biggest takeaway is that scalable observability in 5G environments requires architectural efficiency,...
Read more

Evolving Packet Brokering for Modern Network Observability

Image
  I recently reviewed a technical overview describing how packet brokering platforms are evolving to support large-scale observability in modern data centers and service provider networks. The discussion focused on scalability, automated deployment, and operational efficiency. Some practical observations: • Network visibility platforms are expanding support for 400G switching hardware used in modern data center fabrics • GRE encapsulation enables mirrored traffic to move across Layer-3 networks while preserving packet metadata • Zero-Touch Provisioning simplifies onboarding for new or factory-reset switches • Packet brokers aggregate traffic from TAPs and SPAN ports and apply filtering and distribution policies • CLI improvements help operators manage large monitoring environments more efficiently One notable element was how GRE tunneling extends observability beyond Layer-2 boundaries. By encapsulating mirrored traffic into GRE tunnels, monitoring systems can receive full packet c...
Read more

How Network Copilot Uses Agentic AI to Correlate FortiGate and Splunk

Image
I recently went through a detailed implementation explaining how Network Copilot correlates FortiGate firewall telemetry with operational data indexed in Splunk . What stood out was how grounded the discussion was in real operational workflows rather than theoretical AI claims. Some practical observations: • Network and security logs are typically distributed across multiple platforms • Troubleshooting often requires manually correlating events between systems • Network Copilot ingests metrics, events, logs, and traces from firewall environments • Operators can interact with the system using natural language questions • Explainable root cause analysis improves operational confidence One misconception addressed during the walkthrough was that AI in operations means replacing engineers. In reality, the system augments teams by surfacing insights faster and correlating information across environments. Another interesting point was how the platform learns from real firewall deployments...
Read more