| Architecture and Design |
| ======================= |
| |
| Architecture |
| ------------ |
| |
| Classic SDN |
| ^^^^^^^^^^^ |
| SD-Fabric operates as a hybrid L2/L3 fabric. As a pure (or classic) SDN solution, SD-Fabric does |
| not use any of the traditional control protocols typically found in networking, a non-exhaustive |
| list of which includes: STP, MSTP, RSTP, LACP, MLAG, PIM, IGMP, OSPF, IS-IS, Trill, RSVP, LDP |
| and BGP. Instead, SD-Fabric uses an SDN Controller (ONOS) decoupled from the data plane |
| hardware to directly program ASIC forwarding tables in a pipeline defined by a P4 program. In |
| this design, a set of applications running on ONOS program all the fabric functionality and |
| features, such as Ethernet switching, IP routing, mobile core user plane, multicast, DHCP Relay, |
| and more. |
| |
| |
| Topologies |
| ^^^^^^^^^^ |
| SD-Fabric supports a number of different topological variants. In its simplest instantiation, one |
| could use a single leaf or a leaf-pair to connect servers, external routers, and other equipment |
| like access nodes or physical appliances (PNFs). Such a deployment can also be scaled |
| horizontally into a leaf-and-spine fabric (2-level folded Clos), by adding 2 or 4 spines and up to |
| 10 leaves in single or paired configurations. Further scale can be achieved by distributing the |
| fabric itself across geographical regions, with spine switches in a primary central location, |
| connected to other spines in multiple secondary (remote) locations using WDM links. Such 4-level |
| topologies (leaf-spine-spine-leaf) can be used for backhaul in operator networks, where |
| the secondary locations are deeper in the network and closer to the end-user. In these |
| configurations, the spines in the secondary locations serve as aggregation devices that backhaul |
| traffic from the access nodes to the primary location which typically has the facilities for compute |
| and storage for NFV applications. |
| See :ref:`Topology` for details. |
| |
| |
| Redundancy |
| ^^^^^^^^^^ |
| SD-Fabric supports redundancy at every level. A leaf-spine fabric is redundant by design in the |
| spine layer, with the use of ECMP hashing and multiple spines. In addition, SD-Fabric supports |
| leaf pairs, where servers and external routers can be dual-homed to two ToRs in an active-active |
| configuration. In the control plane, some SDN solutions use single instance controllers, which are |
| single points of failure. Others use two controllers in active backup mode, which is redundant, |
| but may lack scale as all the work is still being done by one instance at any time and scale can |
| never exceed the capacity of one server. In contrast, SD-Fabric is based on ONOS, an SDN |
| controller that offers N-way redundancy and scale. An ONOS cluster with 3 or 5 instances are all |
| active nodes doing work simultaneously, and failure handling is fully automated and completely |
| handled by the ONOS platform. |
| |
| .. image:: images/arch-redundancy.png |
| :width: 350px |
| |
| MPLS Segment Routing (SR) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^ |
| While SR is not an externally supported feature, SD-Fabric architecture internally uses concepts |
| like globally significant MPLS labels that are assigned to each leaf and spine switch. The leaf |
| switches push an MPLS label designating the destination ToR (leaf) onto the IPv4 or IPv6 traffic, |
| before hashing the flows to the spines. In turn, the spines forward the traffic solely on the basis |
| of the MPLS labels. This design concept, popular in IP/MPLS WAN networks, has significant |
| advantages. Since the spines only maintain label state, it leads to significantly less programming |
| burden and better scale. For example, in one use case the leaf switches may each hold 100K+ |
| IPv4/v6 routes, while the spine switches need to be programmed with only 10s of labels! As a |
| result, completely different ASICs can be used for the leaf and spine switches; the leaves can |
| have bigger routing tables and deeper buffers while sacrificing switching capacity, while the |
| spines can have smaller tables with high switching capacity. |
| |
| Beyond Traditional Fabrics |
| -------------------------- |
| |
| .. image:: images/arch-features.png |
| :width: 700px |
| |
| While SD-Fabric offers advancements that go well beyond traditional fabrics, it is first helpful to |
| understand that SD-Fabric provides all the features found in network fabrics from traditional |
| networking vendors in order to make SD-Fabric compatible with all existing infrastructure |
| (servers, applications, etc.). |
| |
| At its core, SD-Fabric is a L3 fabric where both IPv4 and IPv6 packets are routed across server |
| racks using multiple equal-cost paths via spine switches. L2 bridging and VLANs are also |
| supported within each server rack, and compute nodes can be dual-homed to two Top-of-Rack |
| (ToR) switches in an active-active configuration (M-LAG). SD-Fabric assumes that the fabric |
| connects to the public Internet and the public cloud (or other networks) via traditional router(s). |
| SD-Fabric supports a number of other router features like static routes, multicast, DHCP L3 Relay |
| and the use of ACLs based on layer 2/3/4 options to drop traffic at ingress or redirect traffic via |
| Policy Based Routing. But SDN control greatly simplifies the software running on each switch, |
| and control is moved into SDN applications running in the edge cloud. |
| |
| While these traditional switching/routing features are not particularly novel, SD-Fabric’s |
| fundamental embrace of programmable silicon offers advantages that go far beyond traditional |
| fabrics. |
| |
| Programmable Data Planes & P4 |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| SD-Fabric’s data plane is fully programmable. In marked contrast to traditional fabrics, features |
| are not prescribed by switch vendors. This is made possible by P4, a high-level programming |
| language used to define the switch packet processing pipeline, which can be compiled to run at |
| line-rate on programmable ASICs like Intel Tofino (see https://opennetworking.org/p4/). P4 |
| allows operators to continuously evolve their network infrastructure by re-programming the |
| existing switches, rolling out new features and services on a weekly basis. In contrast, traditional |
| fabrics based on fixed-function ASICs are subject to extremely long hardware development |
| cycles (4 years on average) and require expensive infrastructure upgrades to support new features. |
| |
| SD-Fabric takes advantage of P4 programmability by extending the traditional L2/L3 pipeline for |
| switching and routing with specialized functions such as 4G/5G Mobile Core User Plane Function |
| (UPF) and Inband Network Telemetry (INT). |
| |
| 4G/5G Mobile Core User Plane Function (UPF) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| Switches in SD-Fabric can be programmed to perform UPF functions at line rate. The L2/L3 |
| packet processing pipeline running on Intel Tofino switches has been extended to include |
| capabilities such as GTP-U tunnel termination, usage reporting, idle-mode buffering, QoS, slicing, |
| and more. Similar to vRouter, a new ONOS app abstracts the whole leaf-spine fabric as one big |
| UPF, providing integration with the mobile core control plane using a 3GPP-compliant |
| implementation of the Packet Forwarding Control Protocol (PFCP). |
| |
| With integrated UPF processing, SD-Fabric can implement a 4G/5G local breakout for edge |
| applications that is multi-terabit and low-latency, without taking away CPU processing power for |
| containers or VMs. In contrast to UPF solutions based on full or partial smartNIC offload, |
| SDFabric’s embedded UPF does not require additional hardware other than the same leaf and spine |
| switches used to interconnect servers and base stations. At the same time, SD-Fabric can be |
| integrated with both CPU-based or smartNIC-based UPFs to improve scale while supporting |
| differentiated services on a hardware-based fast-path at line rate for mission critical 4G/5G |
| applications (see https://opennetworking.org/sd-core/ for more details). |
| |
| Visibility with Inband Network Telemetry (INT) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| SD-Fabric comes with scalable support for INT, providing unprecedented visibility into how |
| individual packets are processed by the fabric. To this end, the P4-defined switch pipeline has |
| been extended with the ability to generate INT reports for a number of packet events and |
| anomalies, for example: |
| |
| - For each flow (5-tuple), it produces periodic reports to monitor the path in terms of which |
| switches, ports, queues, and end-to-end latency is introduced by each network hop |
| (switch). |
| - If a packet gets dropped, it generates a report carrying the switch ID and the drop reason |
| (e.g., routing table miss, TTL zero, queue congestion, and more). |
| - During congestion, it produces reports to reconstruct a snapshot of the queue at a given |
| time, making it possible to identify exactly which flow is causing delay or drops to other flows. |
| - For GTP-U tunnels, it produces reports about the inner flow, thus monitoring the |
| forwarding behavior and perceived QoS for individual UE flows. |
| |
| SD-Fabric’s INT implementation is compliant with the open source INT specification, and it has |
| been validated to work with Intel’s DeepInsight performance monitoring solution, which acts as |
| the collector of INT reports generated by switches. Moreover, to avoid overloading the INT |
| collector and to minimize the overhead of INT reports in the fabric, SD-Fabric’s data plane uses |
| P4 to implement smart filters and triggers that drastically reduce the number of reports |
| generated, for example, by filtering out duplicates and by triggering report generation only in |
| case of meaningful anomalies (e.g., spikes in hop latency, path changes, drops, queue congestion, |
| etc.). In contrast to other sampling-based approaches which often allow some anomalies to go |
| undetected, SD-Fabric provides precise INT-based visibility that can scale to millions of flows. |
| |
| Flexible ASIC Resource Allocation |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| The P4 program at the base of SD-Fabric’s software stack defines match-action tables for |
| common L2/L3 features such as bridging, IPv4/IPv6 routing, MPLS termination, and ACL, as well |
| as specialized features like UPF, with tables that store GTP-U tunnel information and more. In |
| contrast to fixed-function ASICs used in traditional fabrics, table sizes are not fixed. The use of |
| programmable ASICs like Intel Tofino in SD-Fabric enables the P4 program to be adapted to |
| specific deployment requirements. For example, for routing-heavy deployments, one could |
| decide to increase the IPv4 routing table to take up to 90% of the total ASIC memory, with an |
| arbitrary ratio of longest-prefix match (LPM) entries and exact match /32 entries, while reducing |
| the size of other tables. Similarly, when using SD-Fabric for UPF, one could decide to recompile |
| the P4 program with larger GTP-U tunnel tables, while reducing the IPv4 routing table size to |
| 10-100 entries (since most traffic is tunneled) or by entirely removing the IPv6 tables. |
| |
| Closed Loop Control |
| ^^^^^^^^^^^^^^^^^^^ |
| With complete transparency, visibility, and verifiability, SD-Fabric becomes capable of being |
| optimized and secured through programmatic real-time closed loop control. By defining specific |
| acceptable tolerances for specific settings, measuring for compliance, and automatically adapting |
| to deviations, a closed loop network can be created that dynamically and automatically responds |
| to environmental changes. We can apply closed loop control for a variety of use cases including |
| resource optimization (traffic engineering), verification (forwarding behavior), security (DDoS |
| mitigation), and others. In particular, in collaboration with the Pronto™ project, a microburst |
| mitigation mechanism has been implemented in order to stop attackers from filling up switch |
| queues in an attack attempting to disrupt mission critical traffic. |
| |
| SDN, White Boxes, and Open Source |
| SD-Fabric is based on a purist implementation of SDN in both control and data planes. When |
| coupled with open source, this approach enables faster development of features and greater |
| flexibility for operators to deploy only what they need and customize/optimize the features the |
| way they want. Furthermore, SDN facilitates the centralized configuration of all network |
| functionality, and allows network monitoring and troubleshooting to be centralized as well. Both |
| are significant benefits over traditional box-by-box networking and enable faster deployments, |
| simplified operations, and streamlined troubleshooting. |
| |
| The use of white box (bare metal) switching hardware from ODMs significantly reduces CapEx |
| costs when compared to products from OEM vendors. By some accounts, the cost savings can |
| be as high as 60%. This is typically due to the OEM vendors amortizing the cost of developing |
| embedded switch/router software into the price of their hardware. |
| |
| Finally, open source software allows network operators to develop their own applications and |
| choose how they integrate with their backend systems. And open source is considered more |
| secure, with ‘many eyes’ making it much harder for backdoors to be intentionally or |
| unintentionally introduced into the network. |
| |
| Such unfettered ability to control timelines, features and costs compared to traditional network |
| fabrics makes SD-Fabric very attractive for operators, enterprises, and government applications. |
| |
| Extensible APIs |
| ^^^^^^^^^^^^^^^ |
| People usually think of a network fabric as an opaque pipe where applications send packets into |
| the network and hope they come out the other side. Little visibility is provided to determine |
| where things have gone wrong when a packet doesn't make it to its destination. Network |
| applications have no knowledge of how the packets are handled by the fabric. |
| |
| With the SD-Fabric API, network applications have full visibility and control over how their |
| packets are processed. For example, a delay-sensitive application has the option to be informed |
| of the network latency and instruct the fabric to redirect its packet when there is congestion on |
| the current forwarding path. Similarly, the API offers a way to associate network traffic with a |
| network slice, providing QoS guarantees and traffic isolation from other slices. The API also plays |
| a critical role in closed loop control by offering a programmatic way to dynamically change the |
| packet forwarding behavior. |
| |
| At a high level, SD-Fabric’s APIs fall into four major categories: configuration, information, |
| control, and OAM. |
| |
| - Configuration: APIs let users set up SD-Fabric features such as VLAN information for |
| bridging and subnet information for routing. |
| - Information: APIs allow users to obtain operation status, metrics, and network events |
| of SD-Fabric, such as link congestion, counters, and port status. |
| - Control: APIs enable users to dynamically change the forwarding behavior of the |
| fabric, such as drop or redirect the traffic, setting QoS classification, and applying |
| network slicing policies. |
| - OAM: APIs expose operational and management features, such as software upgrade |
| and troubleshooting, allowing SD-Fabric to be integrated with existing orchestration |
| systems and workflows. |
| |
| Edge-Cloud Ready |
| ---------------- |
| SD-Fabric adopts cloud native technologies and methodologies that are well developed and |
| widely used in the computing world. Cloud native technologies make the deployment and |
| operation of SD-Fabric similar to other software deployed in a cloud environment. |
| |
| Kubernetes Integration |
| ^^^^^^^^^^^^^^^^^^^^^^ |
| Both control plane software (ONOS™ and apps) and, importantly, data plane software (Stratum™), |
| are containerized and deployed as Kubernetes services in SD-Fabric. In other words, not only the |
| servers but also the switching hardware identify as Kubernetes ‘nodes’ and the same processes |
| can be used to manage the lifecycle of both control and data plane containers. For example, Helm |
| charts can be used for installing and configuring images for both, while Kubernetes monitors the |
| health of all containers and restarts failed instances on servers and switches alike. |
| |
| .. image:: images/arch-k8s.png |
| :width: 500px |
| |
| Configuration, Logging, and Troubleshooting |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| SD-Fabric reads all configurations from a single repository and automatically applies appropriate |
| config to the relevant components. In contrast to traditional embedded networking, there is no |
| need for network operators to go through the error-prone process of configuring individual leaf |
| and spine switches. Similarly, logs of each component in SD-Fabric are streamed to an EFK stack |
| (ElasticSearch, Fluentbit, Kibana) for log preservation, filtering and analysis. SD-Fabric offers a |
| single-pane-of-glass for logging and troubleshooting network state, which can further be |
| integrated with operator’s backend systems |
| |
| .. image:: images/arch-logging.png |
| :width: 1000px |
| |
| |
| Monitoring and Alerts |
| ^^^^^^^^^^^^^^^^^^^^^ |
| SD-Fabric continuously monitors system metrics such as bandwidth utilization and connectivity |
| health. These metrics are streamed to Prometheus and Grafana for data aggregation and |
| visualization. Additionally, alerts are triggered when metrics meet predefined conditions. This |
| allows the operators to react to certain network events such as bandwidth saturation even before |
| the issue starts to disrupt user traffic. |
| |
| .. image:: images/arch-monitoring.png |
| :width: 1000px |
| |
| Deployment Automation |
| ^^^^^^^^^^^^^^^^^^^^^ |
| SD-Fabric utilizes a CI/CD model to manage the lifecycle of the software, allowing developers to |
| make rapid iterations when introducing a new feature. New container images are generated |
| automatically when new versions are released. Once the hardware is in place, a complete |
| deployment of the entire SD-Fabric stack can be pushed from the public cloud with a single click |
| fabric-wide in less than two minutes. |
| |
| .. image:: images/arch-deployment.png |
| :width: 900px |
| |
| Aether™-Ready |
| ^^^^^^^^^^^^^ |
| SD-Fabric fits into a variety of edge use cases. Aether is ONF's private 5G/LTE enterprise edge |
| cloud platform, currently running in a dozen sites across multiple geographies as of early 2021. |
| |
| Aether consists of several edge clouds deployed at enterprise sites controlled and managed by a |
| central cloud. Each Aether Edge hosts third-party or in-house edge apps that benefit from low |
| latency and high bandwidth connectivity to the local devices and systems at the enterprise edge. |
| Each edge also hosts O-RAN compliant private-RAN control, IoT, and AI/ML platforms, and |
| terminates mobile user plane traffic by providing local breakout (UPF) at the edge sites. In |
| contrast, the Aether management platform centrally runs the shared mobile-core control plane |
| that supports all edges from the public cloud. Additionally, from a public cloud a management |
| portal for the operator and for each enterprise is provided, and Runtime Operation Control (ROC) |
| controls and configures the entire Aether solution in a centralized manner. |
| |
| SD-Fabric has been fully integrated into the Aether Edge as its underlying network infrastructure, |
| interconnecting all hardware equipment in each edge site such as servers and disaggregated RAN |
| components with bridging, routing, and advanced processing like local breakout. It is worth |
| noting that SD-Fabric can be configured and orchestrated via its configuration APIs by cloud |
| solutions, and therefore can be easily integrated with Aether or third party cloud offerings from |
| hyperscalers. In Aether, SD-Fabric configurations are centralized, modeled, and generated by |
| ROC to ensure the fabric configurations are consistent with other Aether components. |
| |
| In addition to connectivity, SD-Fabric supports a number of advanced services such as |
| hierarchical QoS, network slicing, and UPF idle-mode buffering. And given its native support for |
| programmability, we expect many more innovative services to take advantage of SD-Fabric over |
| time. |
| |
| .. image:: images/arch-aether-ready.png |
| :width: 800px |
| |
| System Components |
| ----------------- |
| |
| .. image:: images/arch-software-stack.png |
| :width: 400px |
| |
| Open Network Operating System (ONOS) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| SD-Fabric uses ONF’s Open Network Operating System (ONOS) as the SDN controller. ONOS is |
| designed as a distributed system, composed of multiple instances operating in a cluster, with all |
| instances actively operating on the network while being functionally identical. This unique |
| capability of ONOS simultaneously affords high availability and horizontal scaling of the control |
| plane. ONOS interacts with the network devices by means of pluggable southbound interfaces. |
| In particular, SD-Fabric leverages P4Runtime™ for programming and gNMI for configuring |
| certain features (such as port speed) in the fabric switches. Like other SDN controllers, ONOS |
| provides several core services like topology discovery and end point discovery (hosts, routers, |
| etc. attached to the fabric). Unlike any other open source SDN controller, ONOS delivers these |
| core services in a distributed way over the entire cluster, such that applications running in any |
| instance of the controller have the same view and information. |
| |
| ONOS Applications |
| ^^^^^^^^^^^^^^^^^ |
| SD-Fabric uses a collection of applications that run on ONOS to provide the fabric features and |
| services. The main application responsible for fabric operation handles connectivity features |
| according to SD-Fabric architecture, while other apps like DHCP relay, AAA, UPF control, and |
| multicast handle more specialized features. Importantly, SD-Fabric uses the ONOS Flow Objective |
| API, which allows applications to program switching devices in a pipeline-agnostic |
| way. By using Flow-Objectives, applications can be written without worrying about low-level |
| pipeline details of various switching chips. The API is implemented by specific device drivers |
| that are aware of the pipelines they serve and can thus convert the application’s API calls to |
| device-specific rules. In this way, the application can be written once, and adapted to pipelines |
| from different ASIC vendors. |
| |
| Stratum |
| ^^^^^^^ |
| SD-Fabric integrates switch software from the ONF Stratum project. Stratum is an open source |
| silicon-independent switch operating system. Stratum implements the latest SDN-centric |
| northbound interfaces, including P4, P4Runtime, gNMI/OpenConfig, and gNOI, thereby enabling |
| interchangeability of forwarding devices and programmability of forwarding behaviors. On the |
| southbound interface, Stratum implements silicon-dependent adapters supporting network |
| ASICs such as Intel Tofino, Broadcom™ XGS® line, and others. |
| |
| Leaf and Spine Switch Hardware |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| In a typical configuration, the leaf and spine hardware used in SD-Fabric are typically Open |
| Compute Project (OCP)™ certified switches from a selection of different ODM vendors. The port |
| configurations and ASICs used in these switches are dependent on operator needs. For example, |
| if the need is only for traditional fabric features, a number of options are possible – e.g., Broadcom |
| StrataXGS ASICs in 48x1G/10G, 32x40G/100G configurations. For advanced needs that take |
| advantage of P4 and programmable ASICs, Intel Tofino or Broadcom Trident 4 are more |
| appropriate choices. |
| |
| ONL and ONIE |
| ^^^^^^^^^^^^ |
| The SD-Fabric switch software stack includes Open Network Linux (ONL) and Open Network |
| Install Environment (ONIE) from OCP. The switches are shipped with ONIE, a boot loader that |
| enables the installation of the target OS as part of the provisioning process. ONL, a Linux |
| distribution for bare metal switches, is used as the base operating system. It ships with a number |
| of additional drivers for bare metal switch hardware elements (e.g., LEDs, SFPs) that are typically |
| unavailable in normal Linux distributions for bare metal servers (e.g., Ubuntu). |
| |
| Docker/Kubernetes, Elasticsearch/Fluentbit/Kibana, Prometheus/Grafana |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| While ONOS/Stratum instances can be deployed natively on bare metal servers/switches, there |
| are advantages in deploying ONOS/Stratum instances as containers and using a container |
| management system like Kubernetes (K8s). In particular, K8s can monitor and automatically |
| reboot lost controller instances (container pods), which then rejoin the operating cluster |
| seamlessly. SD-Fabric also utilizes widely adopted cloud native technologies such as |
| Elastic/Fluentbit/Kibana for log preservation, filtering and analysis, and Prometheus/Grafana for |
| metric monitoring and alert. |