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LF Networking Announces L3AF Release 2.1.0

LF Networking released L3AF 2.1, packed with new features designed to enhance performance, reliability, and flexibility for modern network operations. 

L3AF, a project hosted under LF Networking, is a provider of eBPF-based networking solutions, providing advanced tools for network observability, traffic management, and performance optimization.

This release continues the mission to provide open solutions in eBPF-based networking, now with improved support for cloud native environments, increased observability, and streamlined operational upgrades.

"As we continue to see eBPF emerge as a transformative technology for observability and networking, projects like L3AF play a critical role in pushing the boundaries of what's possible in modern infrastructures," said Arpit Joshipura, GM, Networking, Edge and IoT at the Linux Foundation. "The new features in L3AF 2.1—especially its support for containers and BPF CO-RE—underscore its relevance for cloud native ecosystems and the rapidly growing demand for portable, scalable networking solutions."

"This release is a significant milestone for L3AF and our community," said Patrick Moroney, member of the L3AF Technical Steering Committee. "The introduction of graceful restart and advanced observability features such as kprobes and tracepoints enhances our operational resilience and monitoring capabilities, while container support marks a crucial step toward cloud-native integration. We're excited to see how these new features will help users optimize their networks in real-world scenarios."

Key Highlights of L3AF v2.1:

  • L3AF 2.1 introduces Graceful Restart functionality, allowing seamless upgrades of the L3AF control plane without impacting any running eBPF programs in the data plane.
  • Container Support for l3afd: L3AF can now run within a container, equipped to operate in cloud native environments, improving orchestration and scalability for users leveraging cloud-native platforms.
  • BPF CO-RE in the eBPF Package Repository: L3AF 2.1 now supports BPF CO-RE, enabling portable BPF applications that run across different Linux kernels without modifications.
  • Support for KProbes and Tracepoints: Enhanced observability with support for kprobes and tracepoints, providing deeper kernel-level insights for better eBPF troubleshooting.
  • Dynamically add programs to new interfaces: L3AF 2.1 allows dynamic program attachment to new interfaces, benefiting complex network environments like multi-VM hypervisors.
  • Alternative traffic management options: L3AF now supports attaching eBPF programs to HTB qdisc hooks for refined traffic shaping and resource allocation.
  • Enhanced Logging and Storage for Easier Debugging: Improved logging with local filesystem storage simplifies debugging, accelerates issue resolution, and supports integration with centralized log analysis systems for better observability.
  • L3AF Goes to Dockerhub: L3AF is now available on Docker Hub, making deployment easier within containerized environments for streamlined DevOps integration.

    This release opens the door for improved cloud native integration, zero-downtime upgrades, and expanded monitoring capabilities, making it easier for enterprises to manage and optimize their network infrastructure.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

LF Networking Announces L3AF Release 2.1.0

LF Networking released L3AF 2.1, packed with new features designed to enhance performance, reliability, and flexibility for modern network operations. 

L3AF, a project hosted under LF Networking, is a provider of eBPF-based networking solutions, providing advanced tools for network observability, traffic management, and performance optimization.

This release continues the mission to provide open solutions in eBPF-based networking, now with improved support for cloud native environments, increased observability, and streamlined operational upgrades.

"As we continue to see eBPF emerge as a transformative technology for observability and networking, projects like L3AF play a critical role in pushing the boundaries of what's possible in modern infrastructures," said Arpit Joshipura, GM, Networking, Edge and IoT at the Linux Foundation. "The new features in L3AF 2.1—especially its support for containers and BPF CO-RE—underscore its relevance for cloud native ecosystems and the rapidly growing demand for portable, scalable networking solutions."

"This release is a significant milestone for L3AF and our community," said Patrick Moroney, member of the L3AF Technical Steering Committee. "The introduction of graceful restart and advanced observability features such as kprobes and tracepoints enhances our operational resilience and monitoring capabilities, while container support marks a crucial step toward cloud-native integration. We're excited to see how these new features will help users optimize their networks in real-world scenarios."

Key Highlights of L3AF v2.1:

  • L3AF 2.1 introduces Graceful Restart functionality, allowing seamless upgrades of the L3AF control plane without impacting any running eBPF programs in the data plane.
  • Container Support for l3afd: L3AF can now run within a container, equipped to operate in cloud native environments, improving orchestration and scalability for users leveraging cloud-native platforms.
  • BPF CO-RE in the eBPF Package Repository: L3AF 2.1 now supports BPF CO-RE, enabling portable BPF applications that run across different Linux kernels without modifications.
  • Support for KProbes and Tracepoints: Enhanced observability with support for kprobes and tracepoints, providing deeper kernel-level insights for better eBPF troubleshooting.
  • Dynamically add programs to new interfaces: L3AF 2.1 allows dynamic program attachment to new interfaces, benefiting complex network environments like multi-VM hypervisors.
  • Alternative traffic management options: L3AF now supports attaching eBPF programs to HTB qdisc hooks for refined traffic shaping and resource allocation.
  • Enhanced Logging and Storage for Easier Debugging: Improved logging with local filesystem storage simplifies debugging, accelerates issue resolution, and supports integration with centralized log analysis systems for better observability.
  • L3AF Goes to Dockerhub: L3AF is now available on Docker Hub, making deployment easier within containerized environments for streamlined DevOps integration.

    This release opens the door for improved cloud native integration, zero-downtime upgrades, and expanded monitoring capabilities, making it easier for enterprises to manage and optimize their network infrastructure.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.