The networking industry is undergoing a fundamental architectural transformation, driven by the relentless demands of cloud-scale data centers and the rise of software-defined infrastructure. At the heart of this evolution is the principle of disaggregation: the systematic unbundling of components that were once tightly integrated within proprietary, monolithic systems. 

This movement began with the separation of network hardware from the network operating system (NOS), a paradigm shift championed by hyperscalers to break free from vendor lock-in and accelerate innovation.

In this blog post, we will explore how disaggregated networking takes shape, when the SONiC control plane meets the VPP data plane. You’ll see how their integration creates a fully software-defined router – one that delivers ASIC-class performance on standard x86 hardware, while preserving the openness and flexibility of Linux-based systems.

Disaggregation today extends to the software stack, separating the control plane from the data plane. This decoupling enables modular design, independent component selection, and more efficient performance and cost management.

The integration of Software for Open Networking in the Cloud (SONiC) and the Vector Packet Processing (VPP) framework represents the peak of this disaggregated model. 

SONiC, originally developed by Microsoft and now a thriving open-source project under the Linux Foundation, has established itself as the de facto standard for a disaggregated NOS, offering a rich suite of L3 routing functionalities hardened in the world’s largest data centers.¹ Its core design philosophy is to abstract the underlying switch hardware, allowing a single, consistent software stack to run on a multitude of ASICs from different vendors. This liberates operators from the constraints of proprietary systems and fosters a competitive, innovative hardware ecosystem.

Complementing SONiC’s control plane prowess is VPP, a high-performance, user-space data plane developed by Cisco and now part of the Linux Foundation’s Fast Data Project (FD.io). 

VPP’s singular focus is to deliver extraordinary packet processing throughput on commodity commercial-off-the-shelf (COTS) processors. By employing techniques like vector processing and bypassing the traditional kernel network stack, VPP achieves performance levels previously thought to be the exclusive domain of specialized, expensive hardware like ASICs and FPGAs.

The fusion of these two powerful open-source projects creates a new class of network device: a fully software-defined router that combines the mature, feature-rich control plane of SONiC with the blistering-fast forwarding performance of VPP. 

This architecture directly addresses a critical industry need for a network platform that is simultaneously programmable, open, and capable of line-rate performance without relying on specialized hardware. 

The economic implications are profound. By replacing vertically integrated, vendor-locked routers with a software stack running on standard x86 servers, organizations can fundamentally alter their procurement and operational models. This shift transforms network infrastructure from a capital-expenditure-heavy (CAPEX) model, characterized by large upfront investments in proprietary hardware, to a more agile and scalable operational expenditure (OPEX) model. 

The ability to leverage COTS hardware drastically reduces total cost of ownership (TCO) and breaks the cycle of vendor lock-in, democratizing access to high-performance networking and enabling a more dynamic, cost-effective infrastructure strategy.

Deconstructing the Components: A Tale of Two Titans

To fully appreciate the synergy of the SONiC-VPP integration, it is essential to first understand the distinct architectural philosophies and capabilities of each component. While they work together to form a cohesive system, their internal designs are optimized for entirely different, yet complementary, purposes. SONiC is engineered for control, abstraction, and scalability at the management level, while VPP is purpose-built for raw, unadulterated packet processing speed.

SONiC: The Cloud-Scale Control Plane

SONiC is a complete, open-source NOS built upon the foundation of Debian Linux. Its architecture is a masterclass in modern software design, abandoning the monolithic structure of traditional network operating systems in favor of a modular, containerized, microservices-based approach. This design provides exceptional development agility and serviceability. 

Key networking functions, such as: 

• Border Gateway Protocol (BGP) routing stack 
• Link Layer Discovery Protocol (LLDP)
• platform monitoring (PMON) 

each run within their own isolated Docker container. This modularity allows individual components to be updated, restarted, or replaced without affecting the entire system, a critical feature for maintaining high availability in large-scale environments.

The central nervous system of this distributed architecture is an in-memory Redis database engine, which serves as the single source of truth for the switch’s state. 

Rather than communicating through direct inter-process communication (IPC) or rigid APIs, SONiC’s containers interact asynchronously by publishing and subscribing to various tables within the Redis database. This loosely coupled communication model is fundamental to SONiC’s flexibility. Key databases include:

• CONFIG_DB: Stores the persistent, intended configuration of the switch.
• APPL_DB: A high-level, application-centric representation of the network state, such as routes and neighbors.
• STATE_DB: Holds the operational state of various components.
• ASIC_DB: A hardware-agnostic representation of the forwarding plane’s desired state.

The cornerstone of SONiC’s hardware independence, and the very feature that makes the VPP integration possible, is the Switch Abstraction Interface (SAI). SAI is a standardized C API that defines a vendor-agnostic way for SONiC’s software to control the underlying forwarding elements. A dedicated container, syncd, is responsible for monitoring the ASIC_DB. Upon detecting changes, making the corresponding SAI API calls to program the hardware. Each hardware vendor provides a libsai.so library that implements this API, translating the standardized calls into the specific commands required by their ASIC’s SDK. This elegant abstraction allows the entire SONiC control plane to remain blissfully unaware of the specific silicon it is running on.

VPP: The User-Space Data Plane Accelerator

While SONiC manages the high-level state of the network, VPP is singularly focused on the task of moving packets as quickly as possible. As a core component of the FD.io project, VPP is an extensible framework that provides the functionality of a router or switch entirely in software. Its remarkable performance is derived from several key architectural principles.

Vector Processing

The first and most important is vector processing. Unlike traditional scalar processing, where the CPU processes one packet at a time through the entire forwarding pipeline, VPP processes packets in batches, or “vectors”. A vector typically contains up to 256 packets. The entire vector is processed through the first stage of the pipeline, then the second, and so on. This approach has a profound impact on CPU efficiency. The first packet in the vector effectively “warms up” the CPU’s instruction cache (i-cache), loading the necessary instructions for a given task. The subsequent packets in the vector can then be processed using these cached instructions, dramatically reducing the number of expensive fetches from main memory and maximizing the benefits of modern superscalar CPU architectures.

User-Space Orientation & Kernel Bypass

The second principle is user-space operation and kernel bypass. The Linux kernel network stack, while powerful and flexible, introduces performance overheads from system calls, context switching between kernel and user space, and interrupt handling. VPP avoids this entirely by running as a user-space process. It typically leverages the Data Plane Development Kit (DPDK) to gain direct, exclusive access to network interface card (NIC) hardware. Using poll-mode drivers (PMDs), VPP continuously polls the NIC’s receive queues for new packets, eliminating the latency and overhead associated with kernel interrupts. This direct hardware access is a critical component of its high-throughput, low-latency performance profile.

Packet Processing Graph

Finally, VPP’s functionality is organized as a packet processing graph. Each feature or operation-such as an L2 MAC lookup, an IP4 route lookup, or an Access Control List (ACL) check-is implemented as a “node” in a directed graph. Packets flow from node to node as they are processed. This modular architecture makes VPP highly extensible. New networking features can be added as plugins that introduce new graph nodes or rewire the existing graph, without requiring changes to the core VPP engine.

The design of SAI was a stroke of genius, originally intended to abstract the differences between various hardware ASICs. 

However, its true power is revealed in its application here. The abstraction is so well-defined, that it can be used to represent not just a physical piece of silicon, but a software process. The SONiC control plane does not know or care whether the entity on the other side of the SAI API is a Broadcom Tomahawk chip or a VPP instance running on an x86 CPU. It simply speaks the standardized language of SAI. 

This demonstrates that SAI successfully abstracted away not just the implementation details of a data plane, but the very notion of it being physical, allowing a purely software-based forwarder to be substituted with remarkable elegance.

Feature	SONiC	VPP
Primary Function	Control Plane & Management Plane	Data Plane
Architectural Model	Containerized Microservices	Packet Processing Graph
Key Abstraction	Switch Abstraction Interface (SAI)	Graph Nodes & Plugins
Operating Environment	Kernel/User-space Hybrid (Linux-based)	Pure User-space (Kernel Bypass)
Core Performance Mechanism	Distributed State Management via Redis	Vector Processing & CPU Cache Optimization
Primary Configuration Method	Declarative (config_db.json, Redis)	Imperative (startup.conf, Binary API)

Creating a High-Performance Software Router

The integration of SONiC and VPP is a sophisticated process that transforms two independent systems into a single, cohesive software router. 

The architecture hinges on SONiC’s decoupled state management and a clever translation layer that bridges the abstract world of the control plane with the concrete forwarding logic of the data plane. Tracing the lifecycle of a single route update reveals the elegance of this design.

The End-to-End Control Plane Flow

The process begins when a new route is learned by the control plane. In a typical L3 scenario, this happens via BGP.

1. Route Reception: An eBGP peer sends a route update to the SONiC router. This update is received by the bgpd process, which runs within the BGP container. SONiC leverages the well-established FRRouting (FRR) suite for its routing protocols, so bgpd is the FRR BGP daemon.
2. RIB Update: bgpd processes the update and passes the new route information to zebra, FRR’s core component that acts as the Routing Information Base (RIB) manager.
3. Kernel and FPM Handoff: zebra performs two critical actions. First, it injects a route into the host Linux kernel’s forwarding table – via a Netlink message. Second, it sends the same route information to the fpmsyncd process using the Forwarding Plane Manager (FPM) interface, a protocol designed for pushing routing updates from a RIB manager to a forwarding plane agent.
4. Publishing to Redis: The fpmsyncd process acts as the first bridge between the traditional routing world and SONiC’s database-centric architecture. It receives the route from zebra and writes it into the APPL_DB table in the Redis database. At this point, the route has been successfully onboarded into the SONiC ecosystem.
5. Orchestration and Translation: The Orchestration Agent (orchagent), a key process within the Switch State Service (SWSS) container, is constantly subscribed to changes in the APPL_DB. When it sees the new route entry, it performs a crucial translation. It converts the high-level application intent (“route to prefix X via next-hop Y”) into a hardware-agnostic representation and writes this new state to the ASIC_DB table in Redis.
6. Synchronization to the Data Plane: The final step in the SONiC control plane is handled by the syncd container. This process subscribes to the ASIC_DB. When it detects the new route entry created by orchagent, it knows it must program this state into the underlying forwarding plane.

This entire flow is made possible by the architectural decision to use Redis as a central, asynchronous message bus. 

In a traditional, monolithic NOS, the BGP daemon might make a direct, tightly coupled function call to a forwarding plane driver. This creates brittle dependencies. SONiC’s pub/sub model, by contrast, ensures that each component is fully decoupled. The BGP container’s only responsibility is to publish routes to the APPL_DB; it has no knowledge of who will consume that information. 

This allows the final consumer the data plane-to be swapped out with zero changes to any of the upstream control plane components. This decoupled architecture is what allows VPP to be substituted for a hardware ASIC so cleanly and implies that other data planes could be integrated in the future – simply by creating a new SAI implementation.

The Integration Foundation: libsaivpp.so

The handoff from syncd to the data plane is where the specific SONiC-VPP integration occurs. 

In a standard SONiC deployment on a physical switch, the syncd container would be loaded with a vendor-provided shared library (e.g., libsai_broadcom.so). When syncd reads from the ASIC_DB, it calls the appropriate standardized SAI API function (e.g., sai_api_route->create_route_entry()), and the vendor library translates this into proprietary SDK calls, to program the physical ASIC.

In the SONiC-VPP architecture, this vendor library is replaced by a purpose-built shared library: libsaivpp.so. This library is the critical foundationof the entire system. It implements the full SAI API, presenting the exact same interface tosyncd as any hardware SAI library would. 

However, its internal logic is completely different. When syncd calls a function like create_route_entry(), libsaivpp.so does not communicate with a hardware driver. Instead, it translates the SAI object and its attributes into a binary API message that the VPP process understands. 

It then sends this message to the VPP engine, instructing it to add the corresponding entry to its software forwarding information base (FIB). This completes a “decision-to-execution” loop, bridging SONiC’s abstract control plane with VPP’s high-performance software data plane.

Component (Container)	Key Process(es)	Role in Integration
BGP Container	bgpd	Receives BGP updates from external peers using the FRRouting stack.
SWSS Container	zebra, fpmsyncd	zebra manages the RIB. fpmsyncd receives route updates from zebra and publishes them to the Redis APPL_DB.
Database Container	redis-server	Acts as the central, asynchronous message bus for all SONiC components. Hosts the APPL_DB and ASIC_DB.
SWSS Container	orchagent	Subscribes to APPL_DB, translates application intent into a hardware-agnostic format, and publishes it to the ASIC_DB.
Syncd Container	syncd	Subscribes to ASIC_DB and calls the appropriate SAI API functions to program the data plane.
VPP Platform	libsaivpp.so	The SAI implementation for VPP. Loaded by syncd, it translates SAI API calls into VPP binary API messages.
VPP Process	vpp	The user-space data plane. Receives API messages from libsaivpp.so and programs its internal forwarding tables accordingly.

In the second part of our series, we will move from architecture to action – building and testing a complete SONiC-VPP software router in a containerized lab. 

We’ll configure BGP routing, verify control-to-data plane synchronization, and analyze performance benchmarks that showcase the real-world potential of this disaggregated design.

Sources

1. SONiC (operating system) – Wikipedia https://en.wikipedia.org/wiki/SONiC_(operating_system)
2. Broadcom https://www.broadcom.com/products/ethernet-connectivity/software/enterprise-sonic
3. Vector Packet Processing Documentation – FD.io
   https://docs.fd.io/vpp/21.06/
4. FD.io VPP Whitepaper — Vector Packet Processing Whitepaper https://fd.io/docs/whitepapers/FDioVPPwhitepaperJuly2017.pdf
5. SONiC Virtual Switch with FD.io Vector Packet Processor (VPP) on Google Cloud https://ronnievsmith.medium.com/sonic-virtual-switch-with-fd-ios-vector-packet-processor-vpp-on-google-cloud-89f9c62f5fe3
6. Simplifying Multi-Cloud Networking with SONiC Virtual Gateway https://sonicfoundation.dev/simplifying-multi-cloud-networking-with-sonic-virtual-gateway/
7. Deep dive into SONiC Architecture & Design – SONiC Foundation https://sonicfoundation.dev/deep-dive-into-sonic-architecture-design/
8. Vector Packet Processing – Wikipedia https://en.wikipedia.org/wiki/Vector_Packet_Processing
9. Kernel Bypass Networking with FD.io and VPP — Toonk.io https://toonk.io/kernel-bypass-networking-with-fd-io-and-vpp/index.html
10. PANTHEON.tech*, Delivers Fast Data and Control Planes – Intel® Network Builders https://builders.intel.com/docs/networkbuilders/pantheon-tech-intel-deliver-fast-data-and-control-planes-1663788453.pdf

VPP Guide — PANTHEON.tech
https://pantheon.tech/blog-news/vpp-guide/

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When teams consider deploying Kubernetes, one of the first questions that arises is: where should it run? The default answer is often the public cloud, thanks to its flexibility and ease of use. However, a growing number of organizations are revisiting the advantages of running Kubernetes directly on bare metal servers. For workloads that demand maximum performance, predictable latency, and direct hardware access, bare metal Kubernetes can achieve results that virtualized or cloud-hosted environments simply cannot match.

Why Bare Metal Still Matters

Virtualization and cloud abstractions have delivered convenience, but they also introduce overhead. By eliminating the virtualization layer, applications gain direct access to CPUs, memory, storage devices, and network interfaces. This architectural difference translates into tangible benefits:

• Near-Native Performance – Applications can leverage the full power of the hardware, experiencing minimal overhead from hypervisors or cloud APIs. (Cloud Native Bare Metal Report, CNCF 2023)
  
• Predictable Latency – A critical factor in industries such as real-time analytics, telecommunications, and financial trading, where even microseconds matter.
  
• Efficient Hardware Utilization – GPUs, NVMe storage, or SmartNICs can be accessed directly, without restrictions or performance bottlenecks introduced by virtualization.
  
• Cost Optimization – For workloads that are steady and long-term, owning and operating bare metal servers can be significantly more cost-effective than continuously paying cloud provider bills (IDC: Bare Metal Economics).
  
• Deep Infrastructure Control – Operators can configure firmware, tune networking, and manage storage directly, without depending on the abstractions and limitations imposed by cloud environments.
  

Bare metal provides power and control, but it comes with its own challenge: managing servers at scale. This is precisely where Bare Metal as a Service (BMaaS) steps in.

Bare Metal as a Service with metal-stack.io

metal-stack is an open-source platform that makes bare metal infrastructure as easy to consume as cloud resources. It provides a self-service model for physical servers, automating provisioning, networking, and lifecycle management. Essentially, it transforms racks of hardware into a cloud-like environment—while retaining the performance advantages of bare metal.

Key capabilities of metal-stack.io include:

• Automated Provisioning – Servers can be deployed with clean, reproducible operating system images, similar to how VMs are created in cloud environments.
  
• Integrated Networking – With BGP-based routing and compatibility with Kubernetes CNI plugins like Cilium or Calico, metal-stack ensures high-performance and secure networking. Load balancing can be handled with MetalLB.
  
• Multi-Tenant Support – Physical machines can be securely assigned to different teams or projects, enabling isolation and resource fairness.
  
• Kubernetes-Native Integration – Kubernetes clusters can be provisioned directly onto bare metal nodes via metal-ccm, Gardener, or the Cluster API Provider for Metal-Stack (CAPMS).
  
• Open Source Foundation – The entire stack is open source (MIT/AGPL), ensuring transparency, avoiding vendor lock-in, and allowing teams to adapt the system to their unique needs.
  

By using metal-stack.io, organizations don’t need to compromise between the raw speed of bare metal and the automation of cloud infrastructure—they can have both.

Building the Bare Metal Kubernetes Stack

Deploying Kubernetes on bare metal requires assembling several components into a complete ecosystem. With metal-stack at the foundation, additional layers ensure resilience, security, and operational visibility:

• Networking – Pair metal-stack’s BGP routing with a Kubernetes CNI like Cilium for low-latency, policy-driven communication.
  
• Storage – Tools like Rook (Ceph) or OpenEBS create distributed, high-speed storage pools that can survive node failures.
  
• Observability – Monitoring with Prometheus, and logging with Loki or ELK, provide the insights needed to manage both hardware and workloads effectively.
  
• Security – Without the isolation of virtualization, it becomes essential to enforce RBAC, Pod Security Standards, and strict network policies.
  
• Lifecycle Management – While metal-stack automates the server lifecycle, Kubernetes operators and GitOps tools (e.g., ArgoCD or Flux) help automate application deployment and ongoing operations.
  

This layered approach turns bare metal clusters into production-ready platforms capable of handling enterprise-grade workloads.

Real-World Use Cases

Bare metal Kubernetes shines in scenarios where hardware performance and low latency are non-negotiable. Some standout use cases include:

• AI/ML Training – Direct access to GPUs accelerates machine learning model training and inference workloads (NVIDIA on Bare Metal).
  
• Telecom & 5G Networks – Edge deployments and network functions demand ultra-low latency and predictable performance.
  
• Financial Services – High-frequency trading and other time-sensitive platforms benefit from microsecond-level predictability.
  
• Enterprise Databases – Systems like PostgreSQL or Cassandra achieve higher throughput and stability when running directly on bare metal.
  

In each of these cases, bare metal Kubernetes provides both the performance edge and the flexibility of modern orchestration.

Getting Started with metal-stack.io

For organizations interested in exploring this model, the path forward is straightforward:

1. Explore the metal-stack.io documentation to understand the architecture and requirements.
   
2. Start small with a handful of bare metal servers to build a test cluster.
   
3. Use metal-stack’s Kubernetes integration to deploy a working cluster on these nodes.
   
4. Benchmark workloads against equivalent cloud-based environments to validate performance gains.
   
5. Scale gradually, adding automation and expanding infrastructure as the needs grow.
   

This incremental approach reduces risk and allows teams to build confidence before moving critical workloads.

Conclusion & Next Steps

Running Kubernetes on bare metal delivers unmatched performance, efficiency, and control—capabilities that virtualized and cloud-based environments cannot fully replicate. Thanks to open-source solutions like metal-stack.io, organizations no longer need to choose between raw power and operational simplicity. Bare Metal as a Service (BMaaS) extends the agility of the cloud to physical servers, enabling DevOps teams to manage Kubernetes clusters that are faster, more predictable, and fully under their control.

Ready to explore further?

• Contribute to metal-stack on GitHub
  
• Dive into the documentation
  
• Join the community and share your feedback
  

For high-performance computing, latency-sensitive applications, and hardware-intensive workloads, Kubernetes on bare metal is not just an alternative—it is often the best choice.

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