Cloud Native Architecture – cloud_native

Architectures: A Private Cloud "Kubernetes Service" deployable anywhere.

Fri, 03 Jul 2026 00:00:00 +0000

Relevant projects

ArgoCD

Using since: 2024
Current version: v3.3.6

ArgoCD is used here as our main infrastructure engine. We’ve configured it so it can manage Day 1 and Day 2 operations seamlessly: Cluster APIs primitives on the management cluster to manage control plane operations, and helm applications to manage tooling and configuration of workload clusters. This infrastructure ArgoCD is centralized and dedicated to the platform team.

Cluster API

Using since: 2024
Current version: v1.10.5

Cluster API manages K8s control planes & machines at scale leaning on the ad hoc infrastucture providers (CAPO for OpenStack. CABPT and CACPPT for Talos), and serves as autoscaling & autohealing provider.

External-dns

Using since: 2021
Current version: v2.0.1

External-dns automates Designate DNS records management for workload Clusters.

External Secrets Operator

Using since: 2022
Current version: v1.3.2

External Secrets Operator is the glue between Hashicorp Vault and workload clusters, allowing for secure and centralized secrets management.

Harbor

Using since: 2021
Current version: v2.14

Harbor is the centralized registry: storing and distributing every image used on any container based infrastructure at SNCF.

Kubernetes

Using since: 2018
Current version: v1.35

SNCF’s sole container orchestrator. Used across all hosting zones, including critical infrastructure like high-speed trains.

OpenStack

Using since: 2024
Current version: Yoga

OpenStack provides Machines, DNS, Storage and Network to the platform, it is fully automated by CAPO and therefore abstracted from App Teams.

Renovate

Using since: 2024
Current version: v43

Renovate helps us automate OS and dependency patch management.

Talos Linux

Using since: 2024
Current version: v1.13.2

Talos Linux provides an immutable OS and Kubernetes distribution combo.

TLDR; Synopsis

This reference architecture describes SNCF’s second generation On-Premise Kubernetes Platform, built upon a bare-metal, open source OpenStack Infrastructure to provide at scale Container orchestration and scheduling. It has revolutionized hosting options for the organization’s applications, successfully providing a public cloud-managed Kubernetes service equivalent to internal teams. This platform allows them to build a sovereign strategy aligned with application requirements.

In particular, this architecture aims to show:

How legacy, non-tech organizations can address cloud migration, sovereignty and infrastructure automation in real life.
How to design a Managed Kubernetes Service using a 100 % open-source with zero vendor lock-in approach.
How the proposed architecture is designed for end-to-end automation for all its components.

Organization

SNCF is the publicly owned French rail operator, in charge of every layer of the rail transportation system except actually building trains. It encompasses thousands of different trades, resulting in needing thousands of different software applications operated mostly by one IT department.

The company decided in the late 2010s to move massively to the public cloud, giving birth to a containers platform team leaning on public cloud-managed Kubernetes services to create a hosting solution for container compatible applications. In 2023, that massive public cloud move was mainly wrapped up, the Kubernetes platform entering a growth sustainability phase, with at scale management problematics enforcing GitOps adoption. These stabilization efforts soon highlighted the need to offer the same level of service for container compatible applications not eligible for public cloud migrations, in order to prevent the organization to risk vendor lock-in with a fragmented two-tier system.

This was addressed by a joined initiative between a private cloud provider built on premises and an end-to-end automated kubernetes platform build on top of it.

Teams

Private Cloud Infrastructure is in charge of OpenStack deployment and configuration, managing hardware and network across SNCF’s private hosting zones owned by the SNCF. They offer virtualization, storage, network, LB on which our Kubernetes platform is built.
Cloud Native Integration builds, deploys, and maintains Kubernetes platforms on various hosting zones. We manage infrastructure, tooling and common services integration Apps Teams to deploy their software the easiest and best way possible.
Apps Teams develop, build, and ship applications anywhere in SNCF’s numerous hosting zones.

Architecture

The architecture described below, is our OnPremise implementation of our Kubernetes deployment strategy.

Goals

A unified and streamlined way to deploy and maintain Kubernetes across all our landing zones (private and public clouds).
A centralized end-to-end cluster and tooling lifecycle management.
Security and compliance policies enforcement across all clusters.
A large number of clusters manageable by a small infrastructure team.
Deploying new clusters in minutes.
A maintainable architecture.
100 % open-source.

Can you expand on why you are using those projects/services?

CNCF projects and OpenSource are at the heart of our architecture:

ClusterAPI - Clusters and Machines Management (adopted in 2025): ClusterAPI uses CAPO for lifecycle management of our Kubernetes clusters’ control plane. It also provides through CACPPT machine configuration, enabling node autoscaling and autohealing thanks to cluster autoscaler.
Talos - OS/Kubernetes Combo (Kubernetes: adopted in 2018, Talos: adopted in 2025): We use Kubernetes on every SNCF hosting zone. Talos was chosen as “Kubernetes Operating System” because it is an streamlined way to deploy production-grade clusters with immutability and security in mind. We manage Talos nodes through the Cluster API’s Talos provider (CACPPT).
Openstack - Infrastructure provider (adopted in 2025): OpenStack allow us to consume the bare-metal infrastructure in a cloud native way. It gives us a similar resource abstraction for infrastructure as public clouds. We consume OpenStack through the Cluster API’s Openstack provider (CAPO).
ArgoCD - Clusters and tooling source of truth (adopted in 2023): We are using ArgoCD on all our clusters on private and public clouds. It allows us to manage the lifecycle of clusters infrastructure components.
Kyverno - Policy enforcer and resource mutation (adopted in 2022): Admission policies allows us to enforce compliance with the organization’s policy (e.g., images coming from our private registry, specifying requests/limits). We also leverage resource mutation capabilities to simplify workload best practices for apps teams without having to develop a dedicated controller or operator (e.g., automaticaly configure pod disruption budgets or topology spread constraints).
Cilium - CNI (adopted in 2023): We use Cilium as CNI on all our infrastructures. We especially use it for its network policies capabilities. We may use it for cluster mesh in the future.
Harbor - Private registry (adopted in 2020): Harbor is our private registry used to store OCI artifacts (like images, charts, etc.).
Renovate - Automatic Patch Management (adopted in 2025): Renovate is used to automate patch management for Talos, Kubernetes, and infrastructure components.

What has worked well?

The combination of OpenStack + Kubernetes with Talos + ClusterAPI + ArgoCD allows us to easily build clusters on our on-premise datacenters, and enables cluster provisioning for users in minutes. They provide cloud-native capabilities on-prem, like: resilience, auto-scaling, on-demand load balancing and storage, easy upgrades, etc.

What needs improvement?

The main challenge lies with Cluster API Talos providers, which do not always keep pace with CAPI core releases. This prevents us from upgrading CAPI core to the latest version. We plan to invest engineering time in upstream contributions to the project.

What sort of “glue” have you had to develop to enable usage of your architecture ?

Capix: a controller linking cluster creation by ClusterAPI with the deployment of Kubernetes manifests via ArgoCD. It basically shares the newly generated kubeconfig from ClusterAPI with ArgoCD to deploy day2 manifests on the cluster.
GoIDC: is an OIDC authentication CLI for our on-premises clusters. It allows users to authenticate to their clusters using their corporate credentials.
get-onprem-cluster: is our CLI for users to easily download their kubeconfigs.

Has your architecture evolved? What lessons have you learned from previous iterations?

First iteration: This version started with Terraform, OpenStack, and Talos Linux, but we identified gaps in Day 2 operations efficiency.

Second iteration: We added GitOps using ArgoCD, leveraging the work already done for our other Kubernetes stacks. This improved management of CNI, CSI, security, and monitoring features.

Third iteration: We noticed one missing piece: autoscaling, which was not supported by the standard cluster-autoscaler on OpenStack. As a result, we migrated to Cluster API, which is fully compatible.

The goal was to enable autoscaling, but Cluster API provides even more features and simplifies our workflow. By adding Helm templating, the solution now offers:

Cluster provisioning in just a few dozen minutes.
Native autoscaling capabilities.
Easier node swapping and replacement.
Full cluster upgrades (Kubernetes and/or OS versions) in about 15 minutes.

However, it required significant custom integration work and we encountered several pain points when using the cluster-api-control-plane-provider-talos and cluster-api-bootstrap-provider-talos.

Indeed, we had to invest in these two projects because keeping them up to date with the Cluster API core depends heavily on community efforts.

What’s next for your architecture? What are you looking to do next?

We currently offer single-AZ clusters, as the underlying OpenStack platform operates within one availability zone. We’re working with the OpenStack platform team on a multi-AZ OpenStack deployment to enable multi-AZ cluster support.

Discussion

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Architectures: End-to-End Cloud Native Telco Platform Automation at Swisscom

Mon, 16 Mar 2026 00:00:00 +0000

Relevant Projects

CNCF Projects

Kubernetes

Using since: 2021

The Kubernetes API is the database backend and control plane of the entire automation platform. It acts as the runtime for all CNFs, operators, and platform services. Custom Resource Definitions extend the API to cover telco-specific concerns like IPAM, DNS, and network function configuration.

Flux

Using since: 2022

Flux is the GitOps engine for continuous reconciliation. It monitors Git repositories and synchronizes all desired state — CNF deployment manifests, Custom Resources, DNS endpoints, certificate requests, IP claims, and test definitions — into Kubernetes clusters.

cert-manager

Using since: 2023

Automated certificate lifecycle management integrated with Swisscom’s internal PKI. Certificate requests are expressed as Kubernetes CRs, reconciled by Flux, and managed by cert-manager. Private keys never leave the cluster.

Headlamp

Using since: 2025

Kubernetes dashboard for the management cluster, providing cluster visibility, RBAC-based access control, a CRD documentation browser, and extensible plugin system. Swisscom is listed as an official Headlamp adopter.

SDC (Schema Driven Configuration)

Using since: 2024

Used as the Config Sync Operator to push assembled configurations to CNFs. SDC enables vendor-agnostic, declarative configuration management using YANG schemas and NETCONF/gNMI protocols. Swisscom adopted SDC as its strategic configuration management solution and actively contributes features including config blame, drift detection, validation, testing compatibility with CNFs, and NETCONF Actions support. Swisscom is listed as an official SDC adopter.

CoreDNS

Using since: 2021

In-cluster DNS service discovery for Kubernetes services. Also used with conditional forwarding to route queries for private 5G zones (e.g., 3gppnetwork.org) to the authoritative PowerDNS servers.

ExternalDNS

Using since: 2023

Kubernetes-native automation of DNS records in PowerDNS using Custom Resources and annotations.

MetalLB

Using since: 2022

Load balancer for bare-metal Kubernetes clusters. MetalLB IP address pools are managed via KRM, with IP addresses dynamically allocated from NetBox via the NetBox Operator.

Kubebuilder / controller-runtime

Using since: 2022

Scaffolding framework and libraries for building custom Kubernetes operators. Used to build all domain-specific operators for CNF configuration abstraction, IPAM integration, config synchronization, and DNS automation.

Other Projects

PowerDNS

Using since: 2023

Authoritative DNS server supporting automation of advanced resource records (NAPTR, SRV) required for 5G/SIP via ExternalDNS.

NetBox Operator

Using since: 2024

Kubernetes operator for IPAM integration, open-sourced by Swisscom. Brings IPAM into the Kubernetes API with a claim model inspired by PersistentVolumeClaims — dynamically allocating IP prefixes and addresses from NetBox, managing their lifecycle through Kubernetes garbage collection, and supporting sticky IPs for disaster recovery.

NetBox

Using since: 2023

IP Address Management (IPAM) and network infrastructure modeling. Used as the IPAM backend for dynamic IP allocation across all CNFs and platform services.

TL;DR or Synopsis

Swisscom has built a cloud native telco platform for the end-to-end automation of its 5G standalone core network and cross-domain resource orchestration. The architecture replaces traditional imperative network management (Jenkins pipelines, Ansible playbooks) with a fully declarative, Kubernetes-native automation model driven by GitOps and the Kubernetes Resource Model (KRM).

A mobile core development environment contains approximately 2,000 pods with over 5,000 interdependent configuration parameters across Cloud-Native Network Functions (CNFs) such as UPF, SMF, AMF, UDM, UDR, BSF, NRF, NSSF, and AUSF. Engineers express high-level intents as Kubernetes Custom Resources; custom operators dynamically assemble full configurations at runtime — fetching IP addresses from IPAM, secrets from Vault, certificates from PKI, and infrastructure details from the cluster.

While the 5G core is the primary domain, the orchestration framework extends across multiple network domains and infrastructure services, applying the same intent-based automation patterns consistently.

Organisation

Swisscom is the leading Telecommunications/ISP and ICT company and offers mobile, Internet and TV products, as well as comprehensive IT and digital services to private and business customers. Swisscom’s expertise in cloud native technologies is well-established, as evidenced by its status as a former Gold member and Management Board member of the Cloud Foundry Foundation, along with its certification for Cloud Foundry. Additionally, Swisscom demonstrates a strong commitment to the Open-Source community, having been a CNCF Silver Member for several years and serving as a Kubernetes Certified Service Provider (KCSP) partner. Our skilled employees have delivered numerous talks and presentations at prestigious events such as KubeCon, Cloud Native Zürich, Swiss Cloud Native Day, KCD Suisse Romande, ContainerDays.

The company has embarked on a strategic transformation from a traditional telecom operator (“Telco”) to a technology company (“TechCo”) with 5G as a central driver. Swisscom operates an extensive 5G Non-Standalone (NSA) network covering 99% of the Swiss population. The cloud native platform described here powers the 5G Standalone (SA) core.

Teams

Multiple teams collaborate on this platform:

Cloud Native Resource Orchestration — creates a robust framework for orchestrating cloud-native resources such as CNFs, IPAM, Networks, DNS, Kubernetes Clusters, and more. Designs and operates GitOps pipelines, builds Kubernetes operators, and develops the management cluster UI/UX.
Mobile Cloud Native Engineering — designs, implements, and operates the cloud native 5G core platform, including GitOps pipelines, Kubernetes operators, and network function lifecycle management.
DNS Engineering — builds and operates the highly reliable cloud native DNS service underpinning the 5G core and other infrastructures.
Network Engineering — provides IPAM and Network-as-a-Service.
Platform & Developer Experience — manages Kubernetes clusters and builds developer tooling.

Architecture overview & Goals

Goals

Full GitOps for the 5G Core — Extend GitOps beyond CNF deployment to include network function configuration, certificate management, DNS record provisioning, IP address management, and testing — achieving continuous reconciliation across all layers.
Declarative, Intent-Based Configuration — Replace static, low-level configuration manifests with abstract, intent-driven Custom Resources. Engineers specify what they want using a high level intent (e.g., “this CNF needs an IP address from a subnet in network zone A”) rather than how to achieve it, with Kubernetes operators dynamically assembling configurations at runtime.
Automated CD — High level of automation for telco deployment rollouts. This includes rethinking Change Processes as well as building solid CI/CD/CT pipelines to ensure a highly reliable network.
In-Band with Kubernetes — Bring all automation in-band with the Kubernetes API, eliminating out-of-band tools like Jenkins pipelines and Ansible playbooks. This ensures that the Kubernetes orchestrator has full visibility and control over all resources, enabling self-healing and reconciliation.
Cloud Native DNS Service — Operate a highly reliable, geo-redundant, on-premises DNS service for the 5G core using open-source technologies (CoreDNS, PowerDNS, ExternalDNS), fully automated via GitOps and Kubernetes Custom Resources.
Contribute to the Ecosystem and Shape the Industry Discussion — Open-source key components built or contributed to during this journey (NetBox Operator, SDC, demo code) to enable other organizations to adopt similar patterns. Contribute to Meetups and Conferences in order to achieve broader success of Cloud Native adoption in the Telco Community.

Architecture overview

The platform is organized in three layers:

The Intent Layer stores high-level desired state in Git — engineers define what they want using concise Custom Resources (e.g., a DNN configuration with hostname, region, and SNSSAI).

The Automation Layer runs on Kubernetes and continuously reconciles. Flux pulls intents from Git. Custom operators dynamically assemble full configurations by fetching IP addresses from NetBox (via the NetBox Operator) and creating connectivity in a Network-as-a-Service platform. It then pushes the KRM formatted Runtime Configuration to a Git repository for the Runtime layer to consume.

The Runtime Layer hosts the 5G core CNFs and supporting services. Flux pulls intents from Git. SDC pushes the assembled configuration to CNFs via NETCONF/gNMI. MetalLB, cert-manager, External Secrets Operator, ExternalDNS are used to configure the Workload cluster.

Key Design Principles

GitOps + KRM: Git stores high-level intents; Kubernetes manages dynamic, low-level configuration assembly. This is a shared source of truth across Git and Kubernetes.
Continuous Reconciliation: Every aspect of the system is continuously reconciled against the desired state — following the four OpenGitOps principles.
Abstraction over Complexity: Engineers work with simple, high-level intents; operators handle the complex assembly.

Can you expand on why you are using those projects/services?

From GitOps to KRM

Standard GitOps tools (Flux, Argo CD) combined with Helm/Kustomize have limitations for complex telco use cases: they cannot use live Kubernetes resources as inputs during rendering, cannot invoke custom business logic during template processing, and cannot dynamically assemble configurations from multiple sources. By extending GitOps with the Kubernetes Resource Model (KRM) — Custom Resource Definitions, Custom Resources, and custom Operators — the platform achieves dynamic configuration assembly at runtime.

Configuration Abstraction & Dynamic Assembly

A typical 5G core configuration includes IP addresses, VLAN IDs, DNS records, NF variables, secret references, and certificate references. Previously, engineers had to know all values upfront and embed them statically. The new intent-based model inverts this. The following is an example of a DNN configuration in pseudo-yaml-code.

On the intent layer, only a very stripped-down KRM manifest exists:

apiVersion: telco.swisscom.com/v1
kind: Dnn
spec:
 hostname: "gprs.swisscom.com"
 ipv4: true
 region: ch-east
 type: MobileInternet
 snssai: [1, 10]

This KRM manifest is stored in git and synced to the Management cluster using Flux. From this, the Operators in the automation layer create intermediate resources, in this case an IP Address via NetBox:

apiVersion: netbox.dev/v1
kind: IpAddressClaim
spec:
 parentPrefixSelector:
 region: "ch-east" # from Dnn.spec.region
 family: "IPv4" # from Dnn.spec.ipv4
status:
 ipAddress: 1.2.3.4

The Automation Layer creates the following low level resources for the Runtime Layer:

apiVersion: config.sdcio.dev/v1alpha1
kind: Config
metadata:
 labels:
 config.sdcio.dev/targetName: ch-east  # from Dnn.spec.region
 config.sdcio.dev/targetNamespace: default
spec:
 priority: 10
 config:
 - path: /
 value:
 dnn:
 - name: "gprs.swisscom.com" # from Dnn.spec.hostname
 ip: 1.2.3.4 # from IpAddressClaim.status.ipAddress
 snssai: [1, 10] # from Dnn.spec.snssai
 type: MobileInternet  # from Dnn.spec.type
---
apiVersion: externaldns.k8s.io/v1alpha1
kind: DNSEndpoint
spec:
 endpoints:
 - dnsName: "gprs.swisscom.com" # from Dnn.spec.hostname
 recordType: A
 targets:
 - 1.2.3.4 # from IpAddressClaim.status.ipAddress

SDC will now sync the configuration to the 5G CNF and ExternalDNS will create the DNS records in the authoritative PowerDNS backend.

What has worked well?

Intent-based configuration dramatically reduced the complexity engineers face. Instead of managing thousands of interdependent parameters, they work with concise Custom Resources.
Full GitOps reconciliation across all layers (deployment, configuration, DNS, certificates, IPAM), configuration drift is detected and reverted to ensure consistency.
Custom Kubernetes Operators (built with Kubebuilder/controller-runtime) proved to be the right pattern for telco domain-specific concerns, providing full reconciliation support and native KRM integration.
The claim model for IPAM (NetBox Operator) elegantly solved dynamic IP allocation by following established Kubernetes patterns (PVC analogy).
Bringing all automation in-band with Kubernetes gave the orchestrator full visibility and control, enabling self-healing and eliminating the brittleness of out-of-band tools.
DNS resilience engineering — dedicated hackathons, chaos testing, and disaster recovery playbooks significantly improved DNS service reliability.
Cross-team collaboration on a shared platform and KRM patterns accelerated adoption across multiple network domains.

What has not worked well?

NETCONF as a configuration protocol introduces complexity — it requires SDC as an intermediary and prevents fully Kubernetes-native configuration. Ideally, CNF vendors would support native K8s APIs using CRs/CRDs or Secrets/ConfigMaps.
Tooling gap for KRM-based configuration assembly — no mature, community-standard Kubernetes-native tool exists for dynamic configuration hydration. Swisscom had to build custom operators to fill this gap.
GitOps+KRM auditability trade-off — with dynamically assembled configurations, not all state is visible in Git history. The team continues to explore automated intermediary Git layers.
Cumbersome vendor configuration manifests — large, monolithic configuration files from CNF vendors (with ~5,000 interdependent parameters) required significant effort to decompose into intent-based abstractions.
Telco’s imperative model and Kubernetes’ declarative approach do not align well - SDC follows the declarative paradigm, where users define the desired state and SDC determines the actions to achieve it. In contrast, NETCONF/gNMI use an imperative model that requires explicit ordered steps (“do A, then B, then C”). Translating declarative goals into imperative sequences is complex when user‑defined ordering matters, such as for firewall rules where evaluation order affects behaviour. Example: Issue “Support user sorted lists”

What sort of “glue” have you had to develop?

Custom Kubernetes Operators — domain-specific operators for CNF configuration abstraction, config synchronization, IPAM integration, and DNS automation, all scaffolded with Kubebuilder and controller-runtime.
Configuration hydration logic — the CNF Config Operator dynamically assembles full configurations from multiple sources (NetBox, Vault, cluster environment) based on high-level intents.
SDC contributions — significant development work on SDC including early testing of CNF compatibility, configuration validation, monitoring, config blame, drift detection, and NETCONF Actions support.
ExternalDNS NAPTR support — contributed PR #4212 to enable NAPTR record support for SIP phone calls.
Resilient DNS architecture — created a reference architecture and open sourced on GitHub.
Headlamp plugins — Custom Resource plugin.

How did the Architecture Evolve

Journey

The architecture evolved significantly from traditional imperative automation to the current declarative, KRM-based model:

Phase 1 — Ansible + Jenkins: Initial automation used Ansible playbooks triggered by Jenkins pipelines. Configuration was fire-and-forget with no continuous reconciliation.
Phase 2 — GitOps for deployment: Introduced Flux for CNF deployment, but configuration remained out-of-band via Ansible/NETCONF.
Phase 3 — Full GitOps + KRM: Extended GitOps to cover configuration, DNS, IPAM, certificates, and testing. Built custom operators and adopted SDC for config synchronization. Achieved continuous reconciliation across all layers.

Key lessons

De facto GitOps for operators is not true GitOps — creating Helm releases in Git while configuring NFs via NETCONF out-of-band breaks the GitOps model. Bringing configuration into the Kubernetes API was essential.
Bringing everything in-band with Kubernetes enables self-healing, reconciliation, and eliminates the brittleness of out-of-band tools.
Git as the only source of truth is insufficient — the shared source of truth model (Git for intents, Kubernetes for dynamic state) was a deliberate and necessary evolution.
Abstraction is critical — engineers cannot effectively manage 5,000+ parameters directly. Intent-based CRs with dynamic assembly significantly reduced cognitive load and errors.
Custom Kubernetes Operators are the right pattern for domain-specific concerns that existing tools cannot address.
Contribute upstream — local patches create long-term maintenance burden. Swisscom prioritizes upstream contributions (ExternalDNS, SDC, NetBox Operator) for sustainability.

What’s next for your architecture?

Mature SDC Integration — Continue expanding SDC for full lifecycle management with continuous reconciliation via gNMI and NETCONF, including completion of NETCONF Actions support.
Eliminate NETCONF Dependency — Work with CNF vendors to move toward fully Kubernetes-native configuration APIs, reducing reliance on legacy telco protocols.
Advanced Dynamic Configuration Assembly — Develop more sophisticated Kubernetes operators for multi-source configuration hydration, enabling even more complex intent-based workflows across multiple network domains.
Multi-Cluster & Edge Expansion — Scale the architecture to additional edge locations and Kubernetes clusters while maintaining consistent GitOps-driven automation.
Community Tooling for KRM — Contribute toward a mature, Kubernetes-native tool for dynamic configuration assembly that the wider cloud native community can adopt, addressing the current gap in tooling identified during the project.
Resilience & Reliability — Ongoing improvements to cross-cluster redundancy, disaster recovery playbooks, chaos testing framework, and enhanced monitoring/alerting.
Observability & AIOps — Integrate AI-driven operations capabilities leveraging the rich telemetry data from the platform’s monitoring stack.
Cross-Domain Expansion — Extend the orchestration framework to additional network domains and infrastructure services beyond the 5G core, applying the same intent-based automation patterns consistently.

Community Contributions

Contribution	Details
NetBox Operator	Open-sourced under https://github.com/netbox-community/netbox-operator
SDC Contributions	Active contributor to the SDC project (on its path to CNCF incubation)
KRM Demo Code	https://github.com/swisscom/containerdays-2024-krm
Conference Talks	KubeCon EU, ContainerDays, Open Source Summit EU
CNCF/LFN Whitepaper	Co-authored Accelerating Cloud Native in Telco

Discussion

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Architectures: A Cloud Native Scientific Computing Platform for CERN NextGen AI

Mon, 09 Mar 2026 00:00:00 +0000

Relevant Projects

Argo

ArgoCD is used to manage deployments of all services across multiple clusters and environments. Argo Workflows is used to manage multiple day-2 cluster operations.

Bootc

Bootc provides transactional, in-place operating system images and updates using OCI/Docker container images. Bootc is used to build the minimal base images for our cluster nodes.

ContainerSSH

ContainerSSH offers a SSH frontend to containers/pods running on Kubernetes clusters. Used to expose SSH as a way to access existing sessions in the cluster, with multiple authentication mechanisms offered (Kerberos, OIDC/OAuth2).

Longhorn

Longhorn offers cloud native distributed block storage for Kubernetes. Used to offer in-cluster shared storage to users, with individual and team getting dedicated volumes with read-write-multi access and automated backups.

Kubernetes

Kubernetes provides the required workload scheduling and orchestration for the diverse workloads running in our scientific platform.

Kubeflow

Kubeflow offers tools to manage the complete MLOps lifecycle. Used for profile management and quotas for users and teams, instantiation of notebook servers, pipelines for common and reusable tasks, hyper-parameter tuning with Katib and managing inference endpoints.

Kserve

Kserve standardizes hosting of inference endpoints. Used to encapsulate multiple runtime flavors, such as ONNX, Triton and several others, and offering a declarative way to define inference servers.

Kyverno

Kyverno offers policy as code with support for YAML and CEL based policies. Used as a key component for policy enforcement and mutating workloads according to those policies, adding required settings to expose storage systems, set resources based on GPUs, etc.

Kueue

Kueue is a kubernetes-native system offering advanced scheduling capabilities and quota management. Used to provide job queues and quotas, gang scheduling, fair sharing, among other capabilities.

Prometheus

Prometheus gathers the metrics and insights from all components in the cluster. Used for system and service metrics as well as providing individual workload performance insights on cpu, memory, power and other areas.

stargz-snapshotter

stargz-snapshotter provides lazy pulling of container images. Used to handle efficient job start and execution with image sizes of over 100GB in some cases.

Some additional projects outside the CNCF are essential to this deployment.

MLFlow, a project under the Linux Foundation used for model management and versioning
Nvidia GPU operator to setup and manage drivers and configurations for Nvidia GPUs
AMD GPU operator to setup and manage drivers and configurations for AMD GPUs

TLDR; Synopsis

This reference architecture describes a deployment supporting multiple teams in CERN’s new flagship “Next Generation Triggers” project, looking at innovative computing technologies for data acquisition and processing for the High-Luminosity Large Hadron Collider and beyond.

The cluster and platform target:

Multiple scientific use cases, covering traditional numerical computing as well as machine learning workloads across the different CERN experiments. Scientific computing and in particular high performance computing (HPC) have relied on cloud native tooling for parts of their workloads for several years, but relied on tools like SLURM for advanced scheduling capabilities. This is our first production deployment to offer the full stack based only on cloud native infrastructure
Access to both accelerators (in particular GPUs) as well as specialized nodes such as high CPU core count and high CPU clock frequency. In an earlier stage of experimentation also FPGAs are being integrated targeting fast inference in the CERN experiment online filters
In a shared pool of resources allow interactive access (including SSH, VSCode, Notebooks and just kubectl), traditional batch, MPI and training workloads and inference
Integration with the existing infrastructure at CERN for CI / CD systems (on-premises GitLab and GitHub), identity, and efficient access to multiple storage systems for both user and physics data

A pure cloud native based infrastructure can now successfully serve scientific computing workloads, with advanced scheduling features such as co-scheduling, fair sharing, among others.

Use Cases & Requirements

A set of requirements and use cases was initially defined when designing the architecture. The figure below shows how a shared pool of resources, mostly on-premises but integrating public cloud and supercomputing centers, should be accessed from different services.

Below we highlight specific requirements in terms of hardware and user facing functionality.

Hardware

Support for an heterogeneous set of resources: multiple CPU types, GPUs from multiple vendors, FPGAs and specialized accelerators, all in a shared pool of resources
Integration with multiple network interconnects targeting low latency, including at least Infiniband and RDMA over Converged Ethernet (RoCEv2)
A hybrid deployment integrating external resources, both from public cloud providers and supercomputing centers

User Facing

Curated environments based on container images and maintained by the platform team for the most common user setups, covering ML workloads but also traditional scientific computing. Particularly important has been ensuring these environments are compatible with the existing ways of working, with session environments setup with backwards compatibility for existing physicist tools and scripts
Easily customizable environments, either via dedicated environments maintained by user teams or the ability to install additional packages at runtime. This means users have sudo capabilities inside their sessions
Interactive access to sessions with the ability to choose the amount of GPUs at creation, and a corresponding CPU and memory allocation depending on the type of GPU selected. Once created, access available to the session via notebooks, local vscode instances and most importantly SSH for compatibility with the existing ways of working
Batch access to resources, with support for advanced scheduling capabilities such as queues, quotas, co-scheduling, fair sharing. In addition to the high priority user submissions of training or MPI jobs, the system should be able to backfill unused resources with lower priority workloads to ensure high usage efficiency
Support for the complete machine learning lifecycle, including data preparation, training, hyper-parameter tuning and model inference. In particular, support, efficient integration and automation using common training and tuning frameworks
Model management and versioning, integrated with the rest of the platform with collection and storage of training metadata and logging

Architecture

The diagram below shows how the different projects and tools match the requirements.

Areas of particular interest where effort was required include compute, scheduling, networking, storage and observability.

Compute

Proper isolation and reproducibility is essential for reliable performance and results, removing the effect of noisy neighbors and the latency between CPU and GPU. GPU nodes follow a NUMA-aware dual-socket layout, designed to preserve locality between CPU, memory, and accelerator resources. Each node has two CPU sockets, exposed as two NUMA nodes.

Depending on the node type, GPUs are distributed evenly across these NUMA domains: either 8 GPUs per node, with 4 GPUs attached to each NUMA node, or 4 GPUs per node, with 2 GPUs attached to each NUMA node.

Some relevant configurations to ensure the desired reproducibility and isolation.

CPU and memory resource allocations (requests and limits) scale with the number of GPUs requested by a session: pods receive resources in proportion to the selected GPU count while remaining aligned with the corresponding NUMA locality. This minimizes cross-socket communication, reduces latency between CPU and GPU, and improves the consistency of performance-sensitive workloads

Control CPU Management Policies on the Node, as documented here with the following settings on the kubelet.

cpu-manager-policy=static
cpu-manager-policy-options=full-pcpus-only=true
memory-manager-policy=Static
topology-manager-policy=restricted

Reserved systems resources for kubelet and other add-ons.

system-reserved=cpu=2,memory=1000Mi
reserved-memory=0:memory=1000Mi

Efficient access and distribution of container images, to accelerate the start of sessions based on both curated and custom environments each being multiple 10s of GBs in size. We provide this with a custom daemonset pre-pulling all curated images in advance when published, as well as the ability to do image streaming with the stargz-snapshotter.

Capability to burst out to external resources, in particular public cloud providers and HPC resources.

Scheduling

Bin packing in the scheduling profile instead of the default workload spread across nodes, with strategy MostAllocated ensuring better availability for workloads requiring full nodes.

Advanced scheduling features for queues supporting different resource types and QoS, workload co-scheduling, quotas and fair sharing to optimize overall resource utilization.
Kueue is the main component being used to achieve the advanced scheduling functionality we need.

Networking

Low latency networking such as Infiniband and RDMA over Converged Ethernet (RoCEv2) supporting both traditional CPU and GPU MPI workloads. Currently done by enabling hostNetwork and exposing the corresponding PCI devices for these specific use cases. Driver and lifecycle management of IB/RoCEv2 networking resources is controlled using the Nvidia network operator.

Storage

Users get different storage tiers which fit different usages.

Node local

Very high IOPS but limited space, typically on the low TBs available to all workloads on that node. Not useful for multi-node jobs requiring a shared filesystem. Used also for GPU Direct Storage (GDS) with local NVMEs.

Cluster local

Shared filesystem across all nodes in the cluster, deployed using Longhorn. Limiting the number of network hops as much as possible ensures reasonable IOPS and scales out well in space available with the number of nodes in the cluster (typically 10s of TBs per node). Connection through a single switch for higher performance, as much as possible. Volumes stored in this filesystem are backed up to S3 storage relying on the internal Longhorn backup functionality, with incremental points daily for a week and monthly.

Central

Shared filesystem outside the cluster with much higher storage space available. Managed using CEPH with different IOPS available, up to 2000 guaranteed with bursting to higher values.

Observability

The stack provides visibility into hardware performance, resource efficiency, and environmental impact.

Telemetry Collection

Leveraging a multi-layered collection strategy integrated with the kube-prometheus-stack.

Accelerators: NVIDIA dcgm-exporter and AMD device-metrics-exporter provide deep-field GPU telemetry (utilization, memory, power, temperature, and frequency).

Power & Sustainability: IPMI and Kepler capture hardware-level power metrics. Kepler utilizes RAPL to attribute energy consumption to individual workloads.

System Metrics: Standardized node and container metrics are ingested via Prometheus for a unified view of the cluster.

Visualization and Analysis

Data is exposed via Grafana through three specialized dashboard tiers.

Cluster Overview: Tracks aggregate utilization (CPU, GPU, RAM, Network, Thermals) and node-level health. It highlights idle resources and historical trends to guide capacity planning.

User/Workload Analytics: Provides namespace-filtered views for individual developers to monitor their specific deployments. This view balances resource efficiency (allocated vs. actual usage) with performance profiling (GPU/CPU/RAM saturation) and power consumption, allowing users to independently debug bottlenecks and optimize job performance.

Sustainability Tracking: A dedicated dashboard for CO2-equivalent emissions, offering transparency into the carbon footprint at both the cluster and individual workload levels.

Alerting and Optimization

Alertmanager is configured to trigger notifications for idle resources. By monitoring the delta between allocated requests and actual utilization, the system identifies “zombie” workloads or over-provisioned namespaces, allowing for potential automated or manual resource reclamation to reduce costs and energy waste.

What works particularly well

Workload isolation which is a key aspect when considering needs for reliable benchmarking results. Recent versions of Kubernetes have all the required capabilities to ensure NUMA affinity between CPUs and GPUs, resource pinning to individual workloads and reservation for system services and add-ons.

GPU setup, configuration and monitoring with well supported and up to date operators for both Nvidia and AMD GPUs and automation for metric collection on utilization, power, memory, etc. This includes the initial node configuration required with loading drivers and exposing them to the workloads, as well as day-2 operations such as driver upgrades with integration with the default methods for cordoning and draining nodes.

Kyverno for validation and mutation of cluster resources, allowing a policy based mutation of the resource capabilities based on labels available to users. This ranges from attaching volumes for access to external storage, setting environment variables such as home directories or authentication, automation of resources for cpu and memory and many others. Validation policies also include ensuring users do not attempt invalid NUMA allocations of CPUs and GPUs. Kyverno was chosen after the initial choice of the OPA Gatekeeper had limitations when modifying fields outside the matching location.

What needs improvement

GPU failure detection and integration with the scheduler, either by cordoning nodes or blocking access to faulty GPUs. Depending on the type of fault, the device plugins (for both Nvidia and AMD) may stop exposing faulty devices, but this is not reliable in all cases. Options such as Nvidia Sentinel are being evaluated.

GPU partitioning currently at node level, limiting the ability to have in the same node devices being exposed fully and others being partitioned using MIG. This is currently not supported by the GPU operators, but should be available in the future with the DRA drivers.

Scheduling workloads across multiple clusters, while possible, does not allow seamless access to logs or launching interactive sessions as done for single clusters - `kubectl log` and `kubectl exec` type of request. This is ongoing work in Kueue but currently limits the workloads submitted outside the main cluster to batch-like workloads.

Limited support for checkpoint and restore in several types of workloads, in particular the non machine-learning workloads. This limits the ability to push overall usage of the cluster further up by suspending / preempting idle sessions without losing any work. Efforts such as criu and the checkpoint-restore working group promise to greatly advance the capabilities of the cloud native ecosystem in this area in an workload agnostic way.

Low latency networking with InfiniBand or RoCEv2 in our setup is currently not namespaced and exposed to the users through hostNetwork. In case user workloads are not trusted, other options that provide better network-level isolation should be explored, including SR-IOV and via efforts such as dranet.

What sort of “glue” have you had to develop?

A key goal of our architecture was ensure the complete functionality is available via cloud native APIs, easing the integration with all other tools in the ecosystem. The glue pieces below target ease of use.

Access via SSH was one of the main requests from our users, allowing backwards compatibility with years of custom scripts, continuous integration and several other “ways of working” that require this type of access. We invested internally in developing the required capabilities in the containerssh project, with management of multiple sessions, multiple authenticated methods (OAuth2, Kerberos, X509), among others.

Large number of mutating policies, allowing us to give a better experience to users that do not want to use kubectl or write yaml. Relying on metadata labels in the different resources hides the complexity of setting up volume mounts, environment variables, etc. Our current policies include setting tolerations to assign workloads to specific node flavors, additional environment configurations for MPI workloads, injecting user metadata to access storage systems and interact with internal services, mounting multiple storage systems at CERN or enabling RDMA and GPU Direct Storage.

What’s next for your architecture?

Interactive session management via notebooks, relying on the Kubeflow Notebooks UI. As of today, users require a minimal yaml and usage of the kubectl client to create, list and delete their interactive sessions, even if access is then available via ssh, notebooks, vscode, etc. An upcoming improvement is to offer a UI based interface to manage sessions, likely relying on the Kubeflow Notebook UI but applying to any type of workload.

DRA and automated partitioning in the cluster, as currently we still rely on the Nvidia and AMD operators to manage GPU resources for this particular setup and need to manually set the desired MIG configuration for each node/pool of nodes. This will allow us to have heterogeneous configurations in the same node (with both partitioned and non-partitioned devices) as well as, in the future when the DRA drivers get this functionality, automatic partitioning of devices based on the current workloads.

Bursting to HPC resources, as existing supercomputers and upcoming AI factories have a large number of available GPUs. The main requirement is to integrate with SLURM as an API to manage these remote resources, but in a way that is seamless to users of the service. Projects such as interLink promise to hide the SLURM backends behind the Kubernetes APIs in our platform.

Key Takeaways / Lessons

Adapt to existing ways of working: the success of the platform depends on acceptance by users, who often will not have the time to change their ways of working. Anticipate where effort is needed to meet users where they’re at, building the required glue on top of your cloud native infrastructure.

Iterative and quick development: when exposing a new platform to users with so many stack changes from previous deployments, the ability to iterate very quickly taking into account user feedback is essential. This likely means planning for an intense period after first exposing the services, with the risk of loosing users from the start otherwise.

Upstream first: this is only way to ensure long term sustainability of a platform, exposing requirements and working together with the rest of the community. Local, temporary patches, when required, should be done in parallel with the upstream contributions.

Cloud native is ready for scientific computing and AI/ML: if there were doubts, this experience cleared them up. Cloud native enables the next generation of scientific computing and AI/ML platforms, with all the advanced requirements from high performance computing together with the integration with all modern tools that talk cloud native.

Discussion

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Architectures: A modern and sovereign Private Cloud «Kubernetes Service» for Swiss-based enterprises.

Wed, 04 Mar 2026 00:00:00 +0000

Relevant CNCF projects

Kubernetes

Using since: 2024
Current version: 1.32.8 (CNIP)
Current version: 1.31.x - 1.34.x (SKP)

Kubernetes enables high availability, scalability, and performance for infrastructure, offering a centralized and policy-driven platform to manage network and service data supporting Managed Kubernetes for our cloud customers.

KubeVirt

Using since: 2024
Current version: v1.5.0 (CNIP)

Cluster resources are constructed using KubeVirt for virtual machine abstraction of Control Plane and Worker instances.

Kube-OVN

Using since: 2024
Current version: v1.13.14 (CNIP)

Kube-OVN is utilized as network stack of the infrastructure cluster to enable intra-cluster/east-west network communication of user clusters. It enables a policy-driven security model as well as customer network isolation using VPCs.

MetalLB

Using since: 2024
Current version: v0.15.3

MetalLB is an integral component of the infrastructure deployment process, offering automated access to the framework that provisions individual user cluster resources on bare metal Kubernetes environments.

Container Storage Interface (CSI)

Using since: 2024
Current version: v25.06.3 (trident-csi)
Current version: v0.4.5 (kubevirt-csi)

Kubevirt-CSI is the standard storage interface for persistent volumes in user clusters. Trident-CSI manages NetApp storage requests and can also be used directly to integrate with Swisscom’s File Service Kubernetes, which provides iSCSI and NFS shared storage across all Availability Zones.

Kyverno

Using since: 2024
Current version: v1.13.4 (CNIP)

Kyverno serves as the default policy engine for infrastructure and user clusters, providing robust security constraints. In addition to Kyverno, also Chainsaw (a Kyverno sub-project) is used for automated, declarative e2e testing.

ArgoCD

Using since: 2024
Current version: v3.2.0 (CNIP)

ArgoCD allows us to deliver comprehensive infrastructure using a fully automated GitOps methodology.

Helm

Using since: 2024
Current version: v3.5.1 (CNIP)

Helm automates the creation, packaging, configuration, and deployment of Kubernetes applications by creating reusable charts.

CloudNativePG

Using since: 2024
Current version: v1.27.0 (CNIP)

CloudNativePG (CNPG) manages PostgreSQL databases in cloud-native environments. It handles the full lifecycle of highly available PostgreSQL clusters (primary/standby with native streaming replication), including declarative deployment, scaling, backups, self-healing, failover and monitoring.

Describe your organisation

Swisscom is the leading ICT company in Switzerland and offers mobile, Internet and TV, as well as comprehensive IT and digital services to private and business customers. Swisscom’s expertise in cloud native technologies is well-established, as evidenced by its status as a former Gold member and Management Board member of the Cloud Foundry Foundation, along with its certification for Cloud Foundry. Additionally, Swisscom demonstrates a strong commitment to the Open-Source community, having been a CNCF Silver Member for several years and serving as a Kubernetes Certified Service Provider (KCSP) partner. Our skilled employees have delivered numerous speeches and presentations at prestigious events such as KubeCon, Cloud Native Zürich, Swiss Cloud Native Day, KCD Suisse Romande, ContainerDays, among others.

Our next generation Private Cloud Container as a Service offering «Kubernetes Service» for the B2B market addresses customer’s need for scalable and highly available Kubernetes workload as a flexible and secure IT foundation. It is part of our Swiss-based Enterprise Service Cloud (ESC) market channel as a sovereign, Private Cloud Kubernetes offering for effortless provisioning and usage of our customer’s container workloads.

Describe your entity and/or team

The development and delivery of the new «Kubernetes Service» is done at within Swisscom’s IT-Clouds Value Stream and shared across two teams:

Pathfinders: responsible for the Cloud Native Infrastructure Platform (CNIP). CNIP handles the creation, delivery, and lifecycle management of the KubeVirt-based virtual machines (VMs). These VMs function as nodes for both the Control Plane and Workers. The VMs are ephemeral and can be re-created immediately in case of any failure. They are solely used to enable container-based workloads and do not act as standalone VMs.
Guardians: responsible for the Swisscom Kubernetes Platform (SKP), which runs on top of CNIP. It consists of the installation of Kubermatic Kubernetes Platform (KKP) for the customer tenant (environment) and the setup and support of the highly available Control Plane for any customer (user) cluster.

The layered approach allows Swisscom to manage technological aspects distinctly by segregating the cloud native infrastructure (managed by the Pathfinders team) from the Kubernetes platform (managed by the Guardians team). This strategy ensures considerable flexibility, permitting each layer to be combined or integrated with other technologies in the future.

Brief overview of your architecture and any potential goals you are trying to achieve with it?

Summary

Kubernetes Service is the successor to our current container offering, representing a significant shift towards a more cloud-native approach using advanced Open-Source technology. Currently bound to a vendor-specific implementation, Swisscom has opted to employ open-source tools for the development of cloud native products for customer use. This strategy aims to minimize dependencies and mitigate the risk of vendor lock-ins.

By adopting this architecture, Swisscom can uphold quality within the cloud native domain while maintaining a competitive pricing model due to reduced reliance on external licensing and subscription models. Furthermore, having the ability to develop, maintain, and operate all components internally enhances our decision-making processes and strengthens our roadmap capabilities.

Another important point is that our customers’ data will always remain within Switzerland and under Swiss law. Since we fully own the platform and do not rely on any external vendors, we can confidently guarantee true data sovereignty, hosted entirely on our premises without relying on vague marketing claims. Additionally, because Swisscom is not subject to the US Cloud Act or similar foreign regulations, no non-Swiss legislation can access the data.

Brief overview of architecture

A simplified high-level diagram describes Kubernetes Service, including multi-tenancy and security aspects:

As illustrated in the figure, two separate and independent user tenants, BLUE and RED, are established on shared resources (depicted in yellow), managed by the Kubernetes Infrastructure Cluster. The foundation for all virtual abstractions is the Consolidated Infrastructure (COI) in Swisscom’s data centers.

Each customer-specific environment comprises a management zone (MGMT Zone) and a workload zone. These zones address shared responsibilities, where Swisscom provides the Control Plane for each customer’s environment (illustrated in blue and orange in the next figure).

Customers have the flexibility to deploy workloads within the workload zone independently of the management resources as required. Furthermore, each customer is able to maintain multiple environments. This provides an alternative method for segregating workloads at the tenant level instead of the Kubernetes cluster level, thereby ensuring comprehensive isolation from the outset.

Goals and objectives

One of the primary objectives of the product refresh is to offer more desired features to customers. Compared to the current offering, enhancements include:

Upstream Kubernetes versions with faster updates
Node Autoscaling
Integrated Backup functionalities
Native Kubernetes Load Balancer
Modern customer self-service portal
Additional Kubernetes add-ons available via Application Catalog

Moreover, additional options are directly available to our customers:

Choose from different Container Network Interfaces (CNI)
Access persistent storage through kubevirt-csi

With KubeVirt providing abstraction, KVM is employed as the hypervisor on bare-metal servers. From the customer’s perspective (Customer X), the administrator of their user cluster manages all selections and abstractions shown in the figure below, enabling customers to make independent decisions, e.g. choosing a default CNI from the available options (Cilium, Canal, None).

In addition to technical improvements, we aimed to minimise reliance on external vendors and build a truly sovereign cloud solution that can compete with Public Cloud offerings, free from outside service dependencies. Our goal is for customers to run their Kubernetes workloads in our sovereign ESC Cloud, providing a comprehensive alternative to US hyperscalers - in terms of functionality and, most importantly, data privacy.

Can you expand on why you are using those projects/services?

Cloud-Native Implementation: Utilized CNCF projects and technologies to deploy a comprehensive stack consistent with a microservices-based architecture, resulting in enhanced scalability and operational agility.
Kubernetes for Orchestration: Adopted Kubernetes to manage containerized workloads, enabling automated deployment, scaling, and resilience on management as well as user cluster level.
Kube-OVN as network layer: With Kube-OVN as CNI on the infrastructure clusters and it’s VPC functionality, it allows customer environments to be fully segregated on a shared platform, providing maximum flexibility and strong security enforcement. It enables the teams to use familiar cloud-native development, operations, and debugging tools and skills.
KubeVirt for VM abstraction: A high-quality, Kubernetes-native virtual machine abstraction facilitates the deployment of container-based resources on a centralized cloud-native infrastructure platform, while maintaining flexibility for future use of VM resources.
Open-Source & Cost Efficiency: CNCF components deliver vendor-neutral, cost-effective solutions that are fundamental to container orchestration and observability. These open-source tools form the foundation of our sovereign cloud initiative, empowering internal teams to design customized architectures independently of third-party vendors. Utilizing CNCF technologies allows us to maintain flexibility, scalability, and comprehensive control over our cloud infrastructure, supporting our strategic objectives of autonomy and innovation.
Declarative & Configuration-Driven Approach: CNCF tools align with the low-code/no-code principle by enabling declarative configuration management.

What has worked well?

The implementation has eventually lead to the product launch of Kubernetes Service in August 2025, with some strong outcomes:

Layered Architecture for Enhanced Robustness: The integration of Cloud Native Infrastructure Platform (CNIP) and Swisscom Kubernetes Platform (SKP) forms the foundation of the new Kubernetes Service, enabling flexible handling as separate platform layers for streamlined future operations.
Vendor-Agnostic Production Platform: By eliminating proprietary technology, a resilient and adaptable foundation has been established to host managed Kubernetes clusters within Swisscom’s Private Cloud, ensuring a high degree of flexibility and scalability, as well as privacy.
Modern Cloud-Native Foundation: The implementation of Kubernetes to deliver managed Kubernetes clusters to end customers enables a unified cloud-native stack across all layers of responsibility, promoting consistency and efficiency.
Best Practice Design: Collaborating with Kubermatic, a modern Kubernetes platform was designed, incorporating the latest technologies such as KubeVirt and Kube-OVN, to ensure an enterprise-ready solution for end customers.
Operational Excellence: Equipping teams with essential cloud-native and Kubernetes expertise enhances the attractiveness of Swisscom’s tech stack to potential candidates and reinforces the company’s commitment to the Open-Source community.
Successful Internal Adoption: The Kubernetes Service was successfully launched as Swisscom’s internal Container platform, achieving significant traction with over 60% of workloads migrated within the first 9 months of operation.

What has not worked well?

While the architecture delivered significant improvements, several challenges emerged during implementation:

Enterprise-Readiness of Cloud Native Technologies: Despite successful scaling in test and internal production environments, many advanced cloud-native technologies faced difficulties when deployed in enterprise-grade settings for end customers (e.g., B2B market). This highlighted the need for further refinement and testing in real-world scenarios.

For KubeVirt, for instance, there are only limited real-world examples, best practices or reference designs available to draw upon for large-scale, production-grade business deployments. Switching fully to Kube-OVN as the main network layer also demands extra effort and is less straightforward than traditional network solutions with established production lifecycles.
Limited Support and Professional Services: The availability of professional support, particularly 24/7, for open-source and cloud-native technologies is limited. This poses challenges for enterprises seeking to adopt these technologies and provide services with guaranteed service levels (SLAs).

A possible solution is for more companies to provide professional support and make these services more transparent. Furthermore, the CNCF could introduce a “Certified Supporter” verification system to strengthen trust in firms that offer professional support.
Knowledge Gaps and Skills Requirements: Adopting new technologies demands specialized knowledge and expertise. In-house engineers required additional training and support to effectively maintain and troubleshoot products built on these technologies.
Customer Acceptance and Migration Challenges: Introducing a new platform based on modern technologies, without a proven track record in enterprise-grade deployments, required significant effort to educate customers, facilitate migration from legacy stacks, and promote the benefits of a sovereign cloud solution. This process demanded substantial resources and support to ensure a smooth transition.

What sort of “glue” have you had to develop to enable usage of your architecture ?

The reference architecture provides a strong foundation, making it practical and easy to use. The below elements were designed to simplify adoption, improve usability, and ensure seamless interaction across layers acting as the “glue”:

Unified Abstraction APIs: Developed APIs (Open Service Broker spec) that hide complexity and provide a consistent interface for orchestration and other consuming Operational Support Systems (OSS).
Advanced Routing Functionality: In order to integrate the customer environments into the Swisscom Core Network (MPLS), we developed and implemented our own concept of edge routers using BGP on FRRouting pods. This custom solution supports NAT, Fail-over (VRRP) as well as north-south firewalling (traffic from/to customer environments). These router pods are managed by an operator and configured with custom resource definitions.
Policy Integration Layer: Built operators to dynamically apply and manage Kyverno policies across different stages without requiring deep technical intervention.
Firewall Management: Implemented operators and API endpoints to allow customers to manage firewall rules on the SDN layer of the KubeVirt infrastructure, via Kube-OVN network policies.
Workflow Orchestration Logic: Developed and implemented the entire platform orchestration logic and automated pipelines from bottom-up.
Commandline tooling: Various commandline tools for human operators to manage and control the entire platform and all parts of it with ease.
Testing & Fine-tuning: With limited experience in large-scale bare-metal Kubernetes deployments, we had to do a lot of testing, validation and fine-tuning. We had to make sure, that the platform scales properly with more workloads being migrated every day.

Has your architecture evolved? What lessons have you learned from previous iterations?

Our architecture and product have undergone significant evolution through iterative development, driven by customer feedback and emerging requirements.

Iterative Development Approach: We began by establishing foundational layers and meeting the needs of our internal Swisscom customers. Subsequent iterations introduced advanced features for end customers, incorporating feedback from both internal and external stakeholders.
Continuous Improvement and Feedback Loop: Each iteration allowed us to gather valuable insights and add new functionalities, refining our product and enhancing customer satisfaction.
Steep Learning Curve and Expertise Development: As we ramped up the product, our teams faced a significant learning curve, developing essential expertise and professionalizing DevOps processes to ensure seamless operation.
Strategic Partnerships and Support: Our collaboration with Kubermatic enabled us to leverage professional support for key components, including KubeVirt and Kube-OVN, ultimately maturing our production platform and solidifying its readiness for enterprise-grade deployments.

Through this iterative process, we’ve gained valuable lessons and refined our architecture to better meet the needs of our customers, while developing the expertise and partnerships necessary to drive continued success.

Outcome

By embracing open-source and cloud native technologies, Swisscom successfully created a sovereign cloud solution, modernizing its container offering while reducing vendor lock-in and providing advanced features to customers. The new «Kubernetes Service» demonstrates the power of cloud native architectures in creating flexible, scalable, and cost-effective solutions for enterprise-grade services, all while ensuring true data sovereignty and regulatory compliance. This approach positions Swisscom as a leader in sovereign cloud solutions, offering Swiss (and European) customers a trusted alternative to global hyperscalers.

What’s next for your architecture? What are you looking to do next?

Building on the success of our proven reference architecture, which now supports both internal and external customer workloads in production, we’re focused on expanding and enhancing our offerings:

Hybrid Cloud Expansion and Multi-Cloud Flexibility: We’re working to enable seamless public cloud deployments, complementing our existing Swiss-based data centers and strengthening hybrid cloud use cases.
Edge Cloud Support: With cloud sovereignty in mind, we are developing a «Kubernetes Service On-Prem» extension that will deliver the Private Cloud product on a Cloud Edge Stack at customer premises, enabling an autonomous instance of our Kubernetes Service. This is currently in development with an interested customer.
GPU-Enabled Workloads and Emerging Technologies: Next, we’ll be integrating GPU support and exploring other emerging technologies to unlock new possibilities for compute-intensive applications.
Customer-Driven Features and Enhancements: We’re committed to delivering additional features and functionalities requested by our customers, further enriching our platform and services.
Simplified Onboarding and Resource Optimization: To improve efficiency and resource utilization, we’ll be introducing a shared cluster concept, allowing for more flexible and efficient use of our bare-metal infrastructure.
Exploring New Use Cases - VM Workloads: We’re also investigating the possibility of hosting classical VM workloads on our Cloud Native Infrastructure Platform (CNIP), expanding the platform’s use cases beyond container-based workloads and further increasing its versatility.

By pursuing these initiatives, we aim to continue delivering value to our customers, drive innovation, and grow our architecture and services to meet evolving needs.

Discussion

End user members may participate in the discussion thread for this architecture.