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Charles Chancaebcf32021-09-20 22:17:52 -07001Architecture and Design
2=======================
Charles Chan10ad1442021-10-05 16:57:26 -07003
4Architecture
5------------
6
7Classic SDN
8^^^^^^^^^^^
9SD-Fabric operates as a hybrid L2/L3 fabric. As a pure (or classic) SDN solution, SD-Fabric does
10not use any of the traditional control protocols typically found in networking, a non-exhaustive
11list of which includes: STP, MSTP, RSTP, LACP, MLAG, PIM, IGMP, OSPF, IS-IS, Trill, RSVP, LDP
12and BGP. Instead, SD-Fabric uses an SDN Controller (ONOS) decoupled from the data plane
13hardware to directly program ASIC forwarding tables in a pipeline defined by a P4 program. In
14this design, a set of applications running on ONOS program all the fabric functionality and
15features, such as Ethernet switching, IP routing, mobile core user plane, multicast, DHCP Relay,
16and more.
17
18
19Topologies
20^^^^^^^^^^
21SD-Fabric supports a number of different topological variants. In its simplest instantiation, one
22could use a single leaf or a leaf-pair to connect servers, external routers, and other equipment
23like access nodes or physical appliances (PNFs). Such a deployment can also be scaled
24horizontally into a leaf-and-spine fabric (2-level folded Clos), by adding 2 or 4 spines and up to
2510 leaves in single or paired configurations. Further scale can be achieved by distributing the
26fabric itself across geographical regions, with spine switches in a primary central location,
27connected to other spines in multiple secondary (remote) locations using WDM links. Such 4-level
28topologies (leaf-spine-spine-leaf) can be used for backhaul in operator networks, where
29the secondary locations are deeper in the network and closer to the end-user. In these
30configurations, the spines in the secondary locations serve as aggregation devices that backhaul
31traffic from the access nodes to the primary location which typically has the facilities for compute
32and storage for NFV applications.
33See :ref:`Topology` for details.
34
35
36Redundancy
37^^^^^^^^^^
38SD-Fabric supports redundancy at every level. A leaf-spine fabric is redundant by design in the
39spine layer, with the use of ECMP hashing and multiple spines. In addition, SD-Fabric supports
40leaf pairs, where servers and external routers can be dual-homed to two ToRs in an active-active
41configuration. In the control plane, some SDN solutions use single instance controllers, which are
42single points of failure. Others use two controllers in active backup mode, which is redundant,
43but may lack scale as all the work is still being done by one instance at any time and scale can
44never exceed the capacity of one server. In contrast, SD-Fabric is based on ONOS, an SDN
45controller that offers N-way redundancy and scale. An ONOS cluster with 3 or 5 instances are all
46active nodes doing work simultaneously, and failure handling is fully automated and completely
47handled by the ONOS platform.
48
49.. image:: images/arch-redundancy.png
50 :width: 350px
51
52MPLS Segment Routing (SR)
53^^^^^^^^^^^^^^^^^^^^^^^^^
54While SR is not an externally supported feature, SD-Fabric architecture internally uses concepts
55like globally significant MPLS labels that are assigned to each leaf and spine switch. The leaf
56switches push an MPLS label designating the destination ToR (leaf) onto the IPv4 or IPv6 traffic,
57before hashing the flows to the spines. In turn, the spines forward the traffic solely on the basis
58of the MPLS labels. This design concept, popular in IP/MPLS WAN networks, has significant
59advantages. Since the spines only maintain label state, it leads to significantly less programming
60burden and better scale. For example, in one use case the leaf switches may each hold 100K+
61IPv4/v6 routes, while the spine switches need to be programmed with only 10s of labels! As a
62result, completely different ASICs can be used for the leaf and spine switches; the leaves can
63have bigger routing tables and deeper buffers while sacrificing switching capacity, while the
64spines can have smaller tables with high switching capacity.
65
66Beyond Traditional Fabrics
67--------------------------
68
69.. image:: images/arch-features.png
70 :width: 700px
71
72While SD-Fabric offers advancements that go well beyond traditional fabrics, it is first helpful to
73understand that SD-Fabric provides all the features found in network fabrics from traditional
74networking vendors in order to make SD-Fabric compatible with all existing infrastructure
75(servers, applications, etc.).
76
77At its core, SD-Fabric is a L3 fabric where both IPv4 and IPv6 packets are routed across server
78racks using multiple equal-cost paths via spine switches. L2 bridging and VLANs are also
79supported within each server rack, and compute nodes can be dual-homed to two Top-of-Rack
80(ToR) switches in an active-active configuration (M-LAG). SD-Fabric assumes that the fabric
81connects to the public Internet and the public cloud (or other networks) via traditional router(s).
82SD-Fabric supports a number of other router features like static routes, multicast, DHCP L3 Relay
83and the use of ACLs based on layer 2/3/4 options to drop traffic at ingress or redirect traffic via
84Policy Based Routing. But SDN control greatly simplifies the software running on each switch,
85and control is moved into SDN applications running in the edge cloud.
86
87While these traditional switching/routing features are not particularly novel, SD-Fabric’s
88fundamental embrace of programmable silicon offers advantages that go far beyond traditional
89fabrics.
90
91Programmable Data Planes & P4
92^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
93SD-Fabric’s data plane is fully programmable. In marked contrast to traditional fabrics, features
94are not prescribed by switch vendors. This is made possible by P4, a high-level programming
95language used to define the switch packet processing pipeline, which can be compiled to run at
96line-rate on programmable ASICs like Intel Tofino (see https://opennetworking.org/p4/). P4
97allows operators to continuously evolve their network infrastructure by re-programming the
98existing switches, rolling out new features and services on a weekly basis. In contrast, traditional
99fabrics based on fixed-function ASICs are subject to extremely long hardware development
100cycles (4 years on average) and require expensive infrastructure upgrades to support new features.
101
102SD-Fabric takes advantage of P4 programmability by extending the traditional L2/L3 pipeline for
103switching and routing with specialized functions such as 4G/5G Mobile Core User Plane Function
104(UPF) and Inband Network Telemetry (INT).
105
1064G/5G Mobile Core User Plane Function (UPF)
107^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
108Switches in SD-Fabric can be programmed to perform UPF functions at line rate. The L2/L3
109packet processing pipeline running on Intel Tofino switches has been extended to include
110capabilities such as GTP-U tunnel termination, usage reporting, idle-mode buffering, QoS, slicing,
111and more. Similar to vRouter, a new ONOS app abstracts the whole leaf-spine fabric as one big
112UPF, providing integration with the mobile core control plane using a 3GPP-compliant
113implementation of the Packet Forwarding Control Protocol (PFCP).
114
115With integrated UPF processing, SD-Fabric can implement a 4G/5G local breakout for edge
116applications that is multi-terabit and low-latency, without taking away CPU processing power for
117containers or VMs. In contrast to UPF solutions based on full or partial smartNIC offload,
118SDFabric’s embedded UPF does not require additional hardware other than the same leaf and spine
119switches used to interconnect servers and base stations. At the same time, SD-Fabric can be
120integrated with both CPU-based or smartNIC-based UPFs to improve scale while supporting
121differentiated services on a hardware-based fast-path at line rate for mission critical 4G/5G
122applications (see https://opennetworking.org/sd-core/ for more details).
123
124Visibility with Inband Network Telemetry (INT)
125^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
126SD-Fabric comes with scalable support for INT, providing unprecedented visibility into how
127individual packets are processed by the fabric. To this end, the P4-defined switch pipeline has
128been extended with the ability to generate INT reports for a number of packet events and
129anomalies, for example:
130
131- For each flow (5-tuple), it produces periodic reports to monitor the path in terms of which
132 switches, ports, queues, and end-to-end latency is introduced by each network hop
133 (switch).
134- If a packet gets dropped, it generates a report carrying the switch ID and the drop reason
135 (e.g., routing table miss, TTL zero, queue congestion, and more).
136- During congestion, it produces reports to reconstruct a snapshot of the queue at a given
137 time, making it possible to identify exactly which flow is causing delay or drops to other flows.
138- For GTP-U tunnels, it produces reports about the inner flow, thus monitoring the
139 forwarding behavior and perceived QoS for individual UE flows.
140
141SD-Fabric’s INT implementation is compliant with the open source INT specification, and it has
142been validated to work with Intel’s DeepInsight performance monitoring solution, which acts as
143the collector of INT reports generated by switches. Moreover, to avoid overloading the INT
144collector and to minimize the overhead of INT reports in the fabric, SD-Fabric’s data plane uses
145P4 to implement smart filters and triggers that drastically reduce the number of reports
146generated, for example, by filtering out duplicates and by triggering report generation only in
147case of meaningful anomalies (e.g., spikes in hop latency, path changes, drops, queue congestion,
148etc.). In contrast to other sampling-based approaches which often allow some anomalies to go
149undetected, SD-Fabric provides precise INT-based visibility that can scale to millions of flows.
150
151Flexible ASIC Resource Allocation
152^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
153The P4 program at the base of SD-Fabric’s software stack defines match-action tables for
154common L2/L3 features such as bridging, IPv4/IPv6 routing, MPLS termination, and ACL, as well
155as specialized features like UPF, with tables that store GTP-U tunnel information and more. In
156contrast to fixed-function ASICs used in traditional fabrics, table sizes are not fixed. The use of
157programmable ASICs like Intel Tofino in SD-Fabric enables the P4 program to be adapted to
158specific deployment requirements. For example, for routing-heavy deployments, one could
159decide to increase the IPv4 routing table to take up to 90% of the total ASIC memory, with an
160arbitrary ratio of longest-prefix match (LPM) entries and exact match /32 entries, while reducing
161the size of other tables. Similarly, when using SD-Fabric for UPF, one could decide to recompile
162the P4 program with larger GTP-U tunnel tables, while reducing the IPv4 routing table size to
16310-100 entries (since most traffic is tunneled) or by entirely removing the IPv6 tables.
164
165Closed Loop Control
166^^^^^^^^^^^^^^^^^^^
167With complete transparency, visibility, and verifiability, SD-Fabric becomes capable of being
168optimized and secured through programmatic real-time closed loop control. By defining specific
169acceptable tolerances for specific settings, measuring for compliance, and automatically adapting
170to deviations, a closed loop network can be created that dynamically and automatically responds
171to environmental changes. We can apply closed loop control for a variety of use cases including
172resource optimization (traffic engineering), verification (forwarding behavior), security (DDoS
173mitigation), and others. In particular, in collaboration with the Pronto™ project, a microburst
174mitigation mechanism has been implemented in order to stop attackers from filling up switch
175queues in an attack attempting to disrupt mission critical traffic.
176
177SDN, White Boxes, and Open Source
178SD-Fabric is based on a purist implementation of SDN in both control and data planes. When
179coupled with open source, this approach enables faster development of features and greater
180flexibility for operators to deploy only what they need and customize/optimize the features the
181way they want. Furthermore, SDN facilitates the centralized configuration of all network
182functionality, and allows network monitoring and troubleshooting to be centralized as well. Both
183are significant benefits over traditional box-by-box networking and enable faster deployments,
184simplified operations, and streamlined troubleshooting.
185
186The use of white box (bare metal) switching hardware from ODMs significantly reduces CapEx
187costs when compared to products from OEM vendors. By some accounts, the cost savings can
188be as high as 60%. This is typically due to the OEM vendors amortizing the cost of developing
189embedded switch/router software into the price of their hardware.
190
191Finally, open source software allows network operators to develop their own applications and
192choose how they integrate with their backend systems. And open source is considered more
193secure, with ‘many eyes’ making it much harder for backdoors to be intentionally or
194unintentionally introduced into the network.
195
196Such unfettered ability to control timelines, features and costs compared to traditional network
197fabrics makes SD-Fabric very attractive for operators, enterprises, and government applications.
198
199Extensible APIs
200^^^^^^^^^^^^^^^
201People usually think of a network fabric as an opaque pipe where applications send packets into
202the network and hope they come out the other side. Little visibility is provided to determine
203where things have gone wrong when a packet doesn't make it to its destination. Network
204applications have no knowledge of how the packets are handled by the fabric.
205
206With the SD-Fabric API, network applications have full visibility and control over how their
207packets are processed. For example, a delay-sensitive application has the option to be informed
208of the network latency and instruct the fabric to redirect its packet when there is congestion on
209the current forwarding path. Similarly, the API offers a way to associate network traffic with a
210network slice, providing QoS guarantees and traffic isolation from other slices. The API also plays
211a critical role in closed loop control by offering a programmatic way to dynamically change the
212packet forwarding behavior.
213
214At a high level, SD-Fabric’s APIs fall into four major categories: configuration, information,
215control, and OAM.
216
217- Configuration: APIs let users set up SD-Fabric features such as VLAN information for
218 bridging and subnet information for routing.
219- Information: APIs allow users to obtain operation status, metrics, and network events
220 of SD-Fabric, such as link congestion, counters, and port status.
221- Control: APIs enable users to dynamically change the forwarding behavior of the
222 fabric, such as drop or redirect the traffic, setting QoS classification, and applying
223 network slicing policies.
224- OAM: APIs expose operational and management features, such as software upgrade
225 and troubleshooting, allowing SD-Fabric to be integrated with existing orchestration
226 systems and workflows.
227
228Edge-Cloud Ready
229----------------
230SD-Fabric adopts cloud native technologies and methodologies that are well developed and
231widely used in the computing world. Cloud native technologies make the deployment and
232operation of SD-Fabric similar to other software deployed in a cloud environment.
233
234Kubernetes Integration
235^^^^^^^^^^^^^^^^^^^^^^
236Both control plane software (ONOS™ and apps) and, importantly, data plane software (Stratum™),
237are containerized and deployed as Kubernetes services in SD-Fabric. In other words, not only the
238servers but also the switching hardware identify as Kubernetes ‘nodes’ and the same processes
239can be used to manage the lifecycle of both control and data plane containers. For example, Helm
240charts can be used for installing and configuring images for both, while Kubernetes monitors the
241health of all containers and restarts failed instances on servers and switches alike.
242
243.. image:: images/arch-k8s.png
244 :width: 500px
245
246Configuration, Logging, and Troubleshooting
247^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
248SD-Fabric reads all configurations from a single repository and automatically applies appropriate
249config to the relevant components. In contrast to traditional embedded networking, there is no
250need for network operators to go through the error-prone process of configuring individual leaf
251and spine switches. Similarly, logs of each component in SD-Fabric are streamed to an EFK stack
252(ElasticSearch, Fluentbit, Kibana) for log preservation, filtering and analysis. SD-Fabric offers a
253single-pane-of-glass for logging and troubleshooting network state, which can further be
254integrated with operator’s backend systems
255
256.. image:: images/arch-logging.png
257 :width: 1000px
258
259
260Monitoring and Alerts
261^^^^^^^^^^^^^^^^^^^^^
262SD-Fabric continuously monitors system metrics such as bandwidth utilization and connectivity
263health. These metrics are streamed to Prometheus and Grafana for data aggregation and
264visualization. Additionally, alerts are triggered when metrics meet predefined conditions. This
265allows the operators to react to certain network events such as bandwidth saturation even before
266the issue starts to disrupt user traffic.
267
268.. image:: images/arch-monitoring.png
269 :width: 1000px
270
271Deployment Automation
272^^^^^^^^^^^^^^^^^^^^^
273SD-Fabric utilizes a CI/CD model to manage the lifecycle of the software, allowing developers to
274make rapid iterations when introducing a new feature. New container images are generated
275automatically when new versions are released. Once the hardware is in place, a complete
276deployment of the entire SD-Fabric stack can be pushed from the public cloud with a single click
277fabric-wide in less than two minutes.
278
279.. image:: images/arch-deployment.png
280 :width: 900px
281
282Aether™-Ready
283^^^^^^^^^^^^^
284SD-Fabric fits into a variety of edge use cases. Aether is ONF's private 5G/LTE enterprise edge
285cloud platform, currently running in a dozen sites across multiple geographies as of early 2021.
286
287Aether consists of several edge clouds deployed at enterprise sites controlled and managed by a
288central cloud. Each Aether Edge hosts third-party or in-house edge apps that benefit from low
289latency and high bandwidth connectivity to the local devices and systems at the enterprise edge.
290Each edge also hosts O-RAN compliant private-RAN control, IoT, and AI/ML platforms, and
291terminates mobile user plane traffic by providing local breakout (UPF) at the edge sites. In
292contrast, the Aether management platform centrally runs the shared mobile-core control plane
293that supports all edges from the public cloud. Additionally, from a public cloud a management
294portal for the operator and for each enterprise is provided, and Runtime Operation Control (ROC)
295controls and configures the entire Aether solution in a centralized manner.
296
297SD-Fabric has been fully integrated into the Aether Edge as its underlying network infrastructure,
298interconnecting all hardware equipment in each edge site such as servers and disaggregated RAN
299components with bridging, routing, and advanced processing like local breakout. It is worth
300noting that SD-Fabric can be configured and orchestrated via its configuration APIs by cloud
301solutions, and therefore can be easily integrated with Aether or third party cloud offerings from
302hyperscalers. In Aether, SD-Fabric configurations are centralized, modeled, and generated by
303ROC to ensure the fabric configurations are consistent with other Aether components.
304
305In addition to connectivity, SD-Fabric supports a number of advanced services such as
306hierarchical QoS, network slicing, and UPF idle-mode buffering. And given its native support for
307programmability, we expect many more innovative services to take advantage of SD-Fabric over
308time.
309
310.. image:: images/arch-aether-ready.png
311 :width: 800px
312
313System Components
314-----------------
315
316.. image:: images/arch-software-stack.png
317 :width: 400px
318
319Open Network Operating System (ONOS)
320^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
321SD-Fabric uses ONF’s Open Network Operating System (ONOS) as the SDN controller. ONOS is
322designed as a distributed system, composed of multiple instances operating in a cluster, with all
323instances actively operating on the network while being functionally identical. This unique
324capability of ONOS simultaneously affords high availability and horizontal scaling of the control
325plane. ONOS interacts with the network devices by means of pluggable southbound interfaces.
326In particular, SD-Fabric leverages P4Runtime™ for programming and gNMI for configuring
327certain features (such as port speed) in the fabric switches. Like other SDN controllers, ONOS
328provides several core services like topology discovery and end point discovery (hosts, routers,
329etc. attached to the fabric). Unlike any other open source SDN controller, ONOS delivers these
330core services in a distributed way over the entire cluster, such that applications running in any
331instance of the controller have the same view and information.
332
333ONOS Applications
334^^^^^^^^^^^^^^^^^
335SD-Fabric uses a collection of applications that run on ONOS to provide the fabric features and
336services. The main application responsible for fabric operation handles connectivity features
337according to SD-Fabric architecture, while other apps like DHCP relay, AAA, UPF control, and
338multicast handle more specialized features. Importantly, SD-Fabric uses the ONOS Flow Objective
339API, which allows applications to program switching devices in a pipeline-agnostic
340way. By using Flow-Objectives, applications can be written without worrying about low-level
341pipeline details of various switching chips. The API is implemented by specific device drivers
342that are aware of the pipelines they serve and can thus convert the application’s API calls to
343device-specific rules. In this way, the application can be written once, and adapted to pipelines
344from different ASIC vendors.
345
346Stratum
347^^^^^^^
348SD-Fabric integrates switch software from the ONF Stratum project. Stratum is an open source
349silicon-independent switch operating system. Stratum implements the latest SDN-centric
350northbound interfaces, including P4, P4Runtime, gNMI/OpenConfig, and gNOI, thereby enabling
351interchangeability of forwarding devices and programmability of forwarding behaviors. On the
352southbound interface, Stratum implements silicon-dependent adapters supporting network
353ASICs such as Intel Tofino, Broadcom™ XGS® line, and others.
354
355Leaf and Spine Switch Hardware
356^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
357In a typical configuration, the leaf and spine hardware used in SD-Fabric are typically Open
358Compute Project (OCP)™ certified switches from a selection of different ODM vendors. The port
359configurations and ASICs used in these switches are dependent on operator needs. For example,
360if the need is only for traditional fabric features, a number of options are possible – e.g., Broadcom
361StrataXGS ASICs in 48x1G/10G, 32x40G/100G configurations. For advanced needs that take
362advantage of P4 and programmable ASICs, Intel Tofino or Broadcom Trident 4 are more
363appropriate choices.
364
365ONL and ONIE
366^^^^^^^^^^^^
367The SD-Fabric switch software stack includes Open Network Linux (ONL) and Open Network
368Install Environment (ONIE) from OCP. The switches are shipped with ONIE, a boot loader that
369enables the installation of the target OS as part of the provisioning process. ONL, a Linux
370distribution for bare metal switches, is used as the base operating system. It ships with a number
371of additional drivers for bare metal switch hardware elements (e.g., LEDs, SFPs) that are typically
372unavailable in normal Linux distributions for bare metal servers (e.g., Ubuntu).
373
374Docker/Kubernetes, Elasticsearch/Fluentbit/Kibana, Prometheus/Grafana
375^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
376While ONOS/Stratum instances can be deployed natively on bare metal servers/switches, there
377are advantages in deploying ONOS/Stratum instances as containers and using a container
378management system like Kubernetes (K8s). In particular, K8s can monitor and automatically
379reboot lost controller instances (container pods), which then rejoin the operating cluster
380seamlessly. SD-Fabric also utilizes widely adopted cloud native technologies such as
381Elastic/Fluentbit/Kibana for log preservation, filtering and analysis, and Prometheus/Grafana for
382metric monitoring and alert.