Adding xDS Adapter CFP #14
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| # CFP-30235: xDS Adapter for ClusterIP Routing | ||
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| **SIG:** Agent | ||
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| **Begin Design Discussion:** 2024-01-03 | ||
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| **Cilium Release:** 1.16 | ||
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| **Authors:** Rob Scott <robertjscott@google.com> | ||
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| ## Summary | ||
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| Add a new xDS adapter in Cilium that could take advantage of some of the | ||
| strengths of xDS, particularly the feedback loop via [Load Reporting Service | ||
| (LRS)](https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/load_reporting_service) | ||
| and the overall potential for scalability improvements when adjustments to | ||
| routing configurations don't need to round trip through the Kubernetes API | ||
| Server. This adapter would be an alternative source of endpoints and would not | ||
| replace the existing default behavior of reading directly from Kubernetes APIs. | ||
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| ## Motivation | ||
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| There are some capabilities that the Kubernetes API Server is not well equipped | ||
| to handle. This includes topology aware routing (or any form of routing that | ||
| requires a feedback loop) and routing to endpoints outside of the cluster. | ||
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| ## Goals | ||
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| * Introduce a new xDS adapter to Cilium covering ClusterIP routing capabilities. | ||
| * Develop shared interface between Cilium and Kubernetes, KVStore, and xDS as | ||
| data sources. | ||
| * Provide room for future growth in xDS support beyond ClusterIP routing. | ||
| * Allow the use of any combination of independent data sources (Kubernetes, | ||
| KVStore, xDS) with Cilium. | ||
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robscott marked this conversation as resolved.
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| ## Non-Goals | ||
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| * Build anything additional on top of this xDS adapter. Although this could be a | ||
| foundational technology for several new features, those are out of scope for | ||
| this proposal. | ||
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Member
There was a problem hiding this comment. I don't understand how we can have an xDS adapter without having an implementation of the server / controller.
Contributor
There was a problem hiding this comment. I agree that to validate that this works, we'll need an xDS control plane to talk to. Which is a not-insignificant engineering problem in itself.
Author
There was a problem hiding this comment. Yeah that's a good point. What I'd really intended here was that this CFP was meant to be about foundational building blocks/infrastructure, not features. I agree that an xDS control plane needs to be bundled with this in some way and have already added it as phase 3 of this CFP. |
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| ## Background | ||
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| Several years ago, Matt Klein published a blog post charting a vision for xDS as | ||
| a [universal data plane | ||
| API](https://blog.envoyproxy.io/the-universal-data-plane-api-d15cec7a). Although | ||
| these APIs are primarily used by Envoy and gRPC today, there has been a broader | ||
| goal in the community to enable more widespread usage as part of a truly | ||
| universal data plane API. xDS is CNCF-governed and is gradually transitioning | ||
| content from the | ||
| [envoyproxy/data-plane-api](https://github.com/envoyproxy/data-plane-api) repo | ||
| to [cncf/xds](https://github.com/cncf/xds) repo as part of that overall vision. | ||
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| In parallel to these efforts by the xDS community, GKE is planning to introduce | ||
| xDS as an additional data source for DPv2 configuration. This feels sufficiently | ||
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There was a problem hiding this comment.
Has any public high-level summary been written about this that could be linked here?
Author
There was a problem hiding this comment. Sadly don't have anything public yet, we're still in pretty early stages. |
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| generic and helpful that it could be something that could be contributed to | ||
| upstream Cilium. This could be particularly useful for at least three common use | ||
| cases: | ||
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| 1. Supporting Services and Endpoints from outside of the local cluster. | ||
| 1. Supporting advanced routing techniques, such as topology aware routing. | ||
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| 1. Using load information obtained via bidirectional xDS to steer traffic. | ||
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| Although it is out of scope for this specific CFP to provide complete solutions | ||
| for either of these use cases, it will demonstrate the benefits of having an | ||
| xDS adapter when developing a solution for any of these use cases. | ||
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| ### Use Cases | ||
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| #### Topology Aware Routing | ||
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| As the author of both EndpointSlices and the existing Topology Aware Routing | ||
| approach in Kubernetes, I feel like I’m well equipped to discuss the limitations | ||
| of both. What most people want from Topology Aware Routing is essentially to | ||
| fill the local zone to capacity and then spillover to the next closest set of | ||
| endpoints with capacity. Unfortunately that is very difficult to achieve with | ||
| the existing Kubernetes tooling. There are a few key problems here: | ||
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| 1. Kubernetes has no feedback loop to its dataplane and therefore no reliable | ||
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Member
There was a problem hiding this comment. Just to be clear, in Cilium, an "xDS adapter" alone doesn't solve this. We will also need changes into Cilium's control plane to propagate its internal state back to the xDS adapter.
Author
There was a problem hiding this comment. +1, this just provides the foundation to solve this. The whole idea of topology aware routing or really any form of routing that requires a feedback loop is deceptively simple until you start to think through this part of it. What xDS gives us here is an established API + patterns for completing a feedback loop, but like you're saying, we'd still need to connect the dots here. |
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| way to understand if endpoints have reached capacity. | ||
| 2. Even if there were a feedback loop, spillover to other zones benefits from | ||
| some form of centralized orchestration to avoid a thundering herd problem. | ||
| 3. All changes to endpoint routing currently have to go through Services or | ||
| EndpointSlices, and by extension, the Kubernetes API Server. It would be very | ||
| expensive to implement incremental weighting/spillover adjustments with that | ||
| overhead. For example, adjusting the weight of any individual endpoint would | ||
| require a write to the EndpointSlice API which would then need to be | ||
| distributed to all consumers of that API. That can be problematic from a | ||
| scalability perspective when there are frequent updates paired with a large | ||
| number of endpoints and/or nodes. | ||
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There was a problem hiding this comment. I don't understand this problem. Kubernetes API Server is the "control plane" of the cluster, won't we have this same problem for any "control plane" that receives the load reports from Cilium?
Contributor
There was a problem hiding this comment. This is where the bidirectional nature of xDS comes in a bit more handy - every xDS communication is a message and response pair, unlike the Kubernetes API where each operation is either a write or a read. The design of the Load Reporting Service uses this to its advantage, effectively making the control plane a client instead of the server, since one "give me your load numbers" request will produce a stream of load numbers (that the control plane will simply acknowledge). That said, I think it's important to remember that actually building these control planes is full of really hard concurrency problems. More on that under the "incremental" section in a few lines.
Member
There was a problem hiding this comment. LRS providing feedback on top can only be a net increase in load. I could see maybe an argument there that kube-apiserver protocols are not as well suited to the problem, but before we even consider handling the load feedback, what about the existing load of just configuring K8sEndpoints on all of the cilium-agents? Perhaps some guiding questions:
Author
There was a problem hiding this comment. Let me preface this by saying that scalability was not my primary goal with this CFP, but I do think it could be a nice coincidental benefit of the approach even outside of load reporting. When we get to load reporting, I can't think of any feasible way to do it with existing k8s constructs, and I'd rather not invents something new here when LRS already exists. I spent a fair amount of time thinking about the Kubernetes endpoint scalability problem as I was working on the EndpointSlice API, and went as far as scale testing large k8s clusters until they broke to really understand what was happening (relevant slides). This is a relatively unique problem in terms of Kubernetes APIs. Here's what we have to deal with:
Some Kubernetes APIs have to deal with the scale of EndpointSlices but are only consumed in one or two places - ie some kind of centralized controller(s). The Pod API is consumed by every Node thanks to Kubelet, but Kubelet is able to filter only the Pods that are local to it, meaning that each Pod is distributed to exactly one Node. Let's consider a rolling update of a deployment with 100 Pods. Each of those Pods is going to go through a transition of unready -> ready while the old 100 Pods are transitioning from ready -> unready. Let's say that translates to 400 distinct events to Process (old Pod -> unready, old Pod -> terminated, new Pod -> unready, new Pod -> ready) * 100. In a naive implementation, that would mean that Kubernetes API Server would need to transmit 400 EndpointSlice updates to every node in the cluster. That becomes especially fun when you consider that some providers support 15,000 Nodes per cluster. Fortunately the EndpointSlice controller uses batching to mitigate that to some extent, but hopefully that sets the stage for the unique problem that endpoints pose to the Kubernetes API Server. Like @joestringer mentions above, from the perspective of the API Server, it would be amazing if it didn't need to distribute all of those updates to each individual Node. As far as LRS specifically, +1 to everything @youngnick mentioned above. Load reporting would be especially complex with existing Kubernetes constructs but relatively straightforward with LRS. Everything that goes through Kubernetes API Server would need to be persisted through writes to etcd. On the other hand, LRS combined with an xDS control plane could enable us to bypass that entirely.
I don't think this necessarily does that. It may have some marginal impact here, but I don't think it will be a huge one. In my opinion, a lot of the value here would be that we'd have a path to moving this load from the API Server to a separate control plane that could be scaled independently and potentially more optimized for this specific purpose. My proposal is not really focused on scalability, but I think it provides the potential for significant improvements here. For example, you might choose to deploy an xDS control plane per-zone as a solution that might improve both availability and scalability.
So in the base case where people just don't use this feature, I think there's no impact. Assuming they do, I think it entirely depends on how xDS control planes are deployed. Using the example above, if there were a separate instance of a control plane in each zone it may improve overall availability. On the other hand, if you're adding a single xDS control plane instance to a cluster that has 3 API Server replicas, you might decrease availability. |
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| I believe that xDS is uniquely positioned to address these limitations. | ||
| [LRS](https://www.envoyproxy.io/docs/envoy/latest/api-v3/service/load_stats/v3/lrs.proto) | ||
| (Load Reporting Service) provides a straightforward way to provide load reports | ||
| to a centralized control plane. Similarly, [Delta | ||
| xDS](https://www.envoyproxy.io/docs/envoy/latest/configuration/overview/xds_api#delta-endpoints) | ||
| enables xDS to distribute incremental updates that can be smaller in scope than | ||
| comparable Kubernetes APIs. All of this could be combined to avoid adding | ||
| additional load on the Kubernetes API Server. | ||
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Contributor
There was a problem hiding this comment. I agree that LRS is designed to do exactly this, but implementing incremental xDS is not straightforward. Unless your control plane is very carefully designed, it's nearly impossible (which is why many simpler Envoy control planes don't do it.) To be clear, the hard part is on whoever's building the control plane, not inside the Cilium Agent. But given that we'll need an open-source thing to test with at the very least, the engineering cost of building this shouldn't be underestimated. There was a problem hiding this comment. Yes, which is why the scope of the control plane work should be clear enough and be approached in a phased manner. Implementing something for e2e testing is much simpler than building one usable in production.
Member
There was a problem hiding this comment. Discussing not running this in production gives me a bit of concern. How will Cilium users benefit from this functionality if the reference implementation isn't built with production in mind? There was a problem hiding this comment. To clarify my previous comment, I am not suggesting we should build an experimental control plane. Instead, we should agree on the scope of the work as an initial deliverable. Building a production level control plane that handles all the edge cases can be challenging. There should definitely be a reference implementation that is usable outside of tests.
Author
There was a problem hiding this comment. I think the minimum bar here in terms of initial development would be for a OSS/CNCF reference xDS control plane that is sufficiently reliable to run for e2e testing and development and also at least one that is production ready. Those will probably be the same project, but in the odd chance we are successful at building a good ecosystem here, it's at least theoretically possible that there will be multiple OSS production-ready options and we won't need the reference/testing implementation to also be production ready. Our e2e tests here could become something that could be paired with any compatible xDS control plane in the future. |
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| #### Endpoints From Other Sources | ||
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| In some cases, it’s useful to be able to route to endpoints from other sources, | ||
| such as other Kubernetes clusters or outside of Kubernetes altogether. This can | ||
| be particularly relevant when implementing Multi-Cluster Service routing. | ||
| Implementations today often rely on one of the following approaches: | ||
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| 1. Mirror endpoints from other clusters into Kubernetes by creating custom | ||
| Services and EndpointSlices. | ||
| 2. Introduce Gateways on the edges of clusters that can be used to route | ||
| requests from other clusters. | ||
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| With xDS support, it could be possible to have a centralized xDS control plane | ||
| that was familiar with endpoints in multiple sources, providing a potentially | ||
| more efficient alternative to either of the approaches commonly used today. | ||
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There was a problem hiding this comment. What is it about this centralized xDS control plane that is more efficient? Probably worth also contrasting with kvstoremesh (see CiliumCon @ kubecon NA 2023 or https://arthurchiao.art/blog/trip-first-step-towards-cloud-native-security/#222-kvstoremesh) There was a problem hiding this comment. cilium/cilium#30283 describes the benefits of centralization in a different context. Just wanted to share this for reviewers not aware of the adjacent proposal. |
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| ## Proposal | ||
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| This work will be divided into several phases. Splitting this work into phases | ||
| will enable us to focus on smaller deliverables that each independently provide | ||
| value to the project. | ||
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| ### 1. Common Interface for Data Sources | ||
| Before we get started on an xDS adapter for Cilium, we'll need to build a common | ||
| interface for interacting with data sources (Kubernetes, KVStore, and xDS). This | ||
| interface will enable the majority of the codebase to be entirely unaware of | ||
| which data sources are in use. | ||
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| A key part of defining this interface will involve determining a precedence | ||
| order when more than one data source is connected. When any conflicts occur, | ||
| for example if Services from different sources have the same IP, the following | ||
| order will be used for precedence: | ||
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| 1. Local API | ||
| 2. Kubernetes | ||
| 3. KVStore | ||
| 4. xDS | ||
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| If there is interest, we may make this order configurable in a future | ||
| enhancement. | ||
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| ### 2. New xDS Adapter | ||
| In this phase we'll develop a new xDS adapter that implements the interface | ||
| developed in the first phase. | ||
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| #### API Mapping | ||
| To keep the initial work as focused as possible, I propose using existing xDS | ||
| APIs to model ClusterIP routing. In the future, we may want to consider either | ||
| additions to those existing xDS APIs or new xDS APIs to cover the entirety of | ||
| Cilium functionality. To start, it seems highly valuable to focus on the areas | ||
| where existing xDS APIs overlap with existing Cilium capabilities. | ||
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| This shows how we could use existing xDS APIs to represent Cilium capabilities | ||
| for ClusterIP routing: | ||
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There was a problem hiding this comment. Could you link the Cilium API this is modeled to target?
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There was a problem hiding this comment. This is currently referring to Cilium's Service cache, but I expect that at least some details may change a bit if we're building a common interface around that as part of phase 1: https://github.com/cilium/cilium/blob/0632058f820c05013dfcb010d6bda0911b1269d7/pkg/service/service.go#L73-L97 There was a problem hiding this comment. A bit confusing with so many Service types within the code, but can this be mapped 1:1 to loadbalancer.SVC? |
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| | Cilium Field | Origin xDS | Comments | | ||
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| | Frontend.Address | `filter_chain_match.prefix_ranges` | | | ||
| | Frontend.Port | `filter_chain_match.destination_port` | | | ||
| | Frontend.Protocol | `cluster.metadata.protocol` | | | ||
| | Backend[*].FEPortName | `cluster.metadata.port_name` | | | ||
| | Backend[*].NodeName | ? | TBD - This is only used for logic to determine if an endpoint is local. Instead of sending this through xDS for every endpoint we could just look at the CIDR configured for each Cilium instance. | | ||
| | Backend[*].Addr, Backend[*].Port | `lb_endpoints.endpoint.address.socket_address` | | | ||
| | Backend[*].State | `lb_endpoints.health_status` | | | ||
| | Backend[*].Preferred | N/A | Not used in Kubernetes | | ||
| | Backend[*].Weight | `lb_endpoints.endpoint.load_balancing_weight` | | | ||
| | Type | N/A | Out of scope - only focusing on ClusterIP Services | | ||
| | ExternalTrafficPolicy, InternalTrafficPolicy | `cluster.load_balancing_policy` | Defined in the typed struct `type.googleapis.com/cilium_io_traffic_policy` | | ||
| | NatPolicy | N/A | Not used in Kubernetes | | ||
| | SessionAffinity | `tcpproxy.hash_policy` | | | ||
| | SessionAffinityTimeout | ? | TBD - This represents the maximum session stickiness time. There is no natural place for this in existing xDS APIs. | | ||
| | HealthCheckNodePort | N/A | Out of scope - only focusing on ClusterIP Services | | ||
| | Name | `cluster.metadata.service_name` | | | ||
| | Namespace | `cluster.metadata.service_namespace` | | | ||
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robscott marked this conversation as resolved.
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| ### 3. Sample xDS Control Plane | ||
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Member
There was a problem hiding this comment. Food for thought: it might make sense to add this xDS Control plane into Cilium Operator. There was a problem hiding this comment. +1 if the community decides to provide this to Cilium users.
Contributor
There was a problem hiding this comment. I'm -1 on adding it into the Operator, personally, because the operator is currently tightly focussed on its current role: reading from Kubernetes objects, building some internal model, then writing back to Kubernetes. The Operator doesn't have any actual routing logic itself. An xDS control plane will need to have a lot of routing logic added. I think it's better to keep this as a separate thing that's built with the same tooling as the Agent and Operator (that is, using the Hive and Cell pattern), so that it can be merged in later if necessary. There was a problem hiding this comment. Seems reasonable to have a standlone component that implements the control plane. Adding it to the operator has the benefit of not requiring a new component and it can simply be feature gated. Though from a stability perspective, keeping this out of operator is beneficial. |
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| It would be useful for both the overall OSS experience and e2e testing to have | ||
| an OSS xDS Control Plane that could serve as a reference xDS control plane | ||
| implementation with Cilium. Of course there will likely be multiple options | ||
| here, but it will be helpful to have at least a reference implementation for the | ||
| purposes of e2e testing. | ||
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| ### 4. Report Metrics via LRS | ||
| One of the significant benefits of xDS is that it contains a load reporting | ||
| mechanism. This can enable advanced routing capabilities at the xDS control | ||
| plane layer, such as the topology aware routing example mentioned above. Within | ||
| Cilium we'd likely want to report new and active connections to an xDS control | ||
| plane via LRS. | ||
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There was a problem hiding this comment. Just to note (partly coming from the session we had earlier in the week): I think it makes sense to clearly outline what sort of stats we are looking for, format, etc. and also acknowledge that this may require datapath changes. I suspect that this may end up being a logically fairly separate change that could have other debuggability benefits even in existing environments if exposed through prometheus or other relevant interfaces. |
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| ## Impacts / Key Questions | ||
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| ### Key Question: Should we use a custom API on top of xDS-TP instead? | ||
| Instead of building on top of existing xDS APIs, we could use a custom API on | ||
| top of xDS-TP (xDS Transport Protocol). This would follow the pattern that | ||
| Istio's Ztunnel project used when they developed | ||
| [WDS](https://github.com/istio/ztunnel/blob/db0a74212c42c66b611a71a9613afb501074e257/proto/workload.proto). | ||
| We could also just choose to send Cilium-specific protos over the wire. For | ||
| example, we could use Cilium agent API JSON types encoded using the proto Struct | ||
| type, such that the adapter would be responsible for unmarshalling it to the | ||
| Cilium API Go types (via JSON). | ||
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| #### Pros | ||
| * Very flexible and extensible, can specify or add ~anything | ||
| * Resulting API would likely be very focused on Cilium | ||
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| #### Cons | ||
| * Existing xDS APIs already cover a large portion of Cilium functionality, | ||
| especially ClusterIP routing | ||
| * Unlikely to be portable across different data planes or control planes - it | ||
| would be unlikely to work with any existing xDS control planes | ||
| * Building consensus around new APIs is a non-trivial effort | ||
| * We can add new features and capabilities to existing xDS APIs, we don't need | ||
| to reinvent the wheel | ||
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| Although it's certainly possible that we may run into a point where we want to | ||
| augment existing xDS APIs with custom extensions or move to an entirely custom | ||
| set of xDS APIs, that does not seem like a prudent starting point. Instead we | ||
| recommend taking advantage of the broad intersection between existing xDS APIs | ||
| and existing Cilium features. | ||
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