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Our goal here at Solo.io is to bring valuable solutions to our customers around application networking and service connectivity. Back in October, we announced our plans to enhance our enterprise service-mesh product (Gloo Mesh Enterprise) with eBPF to optimize the functionality around networking, observability, and security. To what extent can eBPF play a role in a service mesh? How does the role of the service proxy change? In this blog we will dig into the role of eBPF for a service mesh data plane and what are some of the tradeoffs of various data-plane architectures.

Envoy proxy has become the de-facto proxy for service mesh implementations and has very good support for Layer 7 capabilities that most of our customers need. Although eBPF and the Kernel can be used to improve the execution of the network (short circuiting optimal paths, offloading TLS/mTLS, observability collection, etc), complex protocol negotiations, parsing, and user-extensions can remain in user space. For the complexities of Layer 7, Envoy remains the data plane for the service mesh.

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Another consideration when attempting to optimize the data path for a service mesh is whether to run a sidecar per workload or to use a single, shared proxy per node. For example when running massive clusters with hundreds of pods and thousands of nodes, a shared-proxy model can deliver optimizations around memory and configuration overhead. But is this the right approach for everyone? Absolutely not. For many enterprise users, some memory overhead is worth the better tenancy and workload isolation gains with sidecar proxies.

In this model, we deploy a sidecar proxy with each application instance. The sidecar has all of the configurations it needs to route traffic on behalf of the workload and can be tailored to the workload.

This model does give the best feature isolation to reduce the blast radius of any noisy neighbors. Misconfigured or app-specific buffers/connection-pooling/timeouts are isolated to a specific workload. Extensions using Lua or Wasm (that could potentially take down a proxy) are also constrained to specific workloads.

From a security perspective, we originate and terminate connections directly with the applications. We can use the mTLS capabilities of the service mesh to prove the identity of the services on both ends of the connections scoped down to the level of the application process. We can then write fine-grained authorization policies based on this identity. Another benefit of this model comes if a single proxy ends up victim to an attacker, the compromised proxy is isolated to a specific workload; the blast radius is limited. On the downside, however, since sidecars must be deployed with the workload, there is the possibility that a workload opts not to inject the sidecar, or worse, finds a way to work around the sidecar.

The shared-proxy per node introduces optimizations that make sense for large clusters where memory overhead is a top concern and amortization of the cost of the memory is desirable. In this model, instead of each sidecar proxy configured with the routing and clusters needed to route traffic, that configuration is shared across all workloads on a node in a single proxy.

From a feature isolation perspective, you end up trying to solve all of the concerns for all of the workload instances in one process (one Envoy proxy) and this can have drawbacks. For example, could application configurations across multiple apps conflict with each other or have offsetting behaviors in the proxy? Can you safely load secrets or private-keys that must be separated for regulatory reasons? Can you deploy Wasm extensions without the risk of affecting the behavior of the proxy for other applications? Sharing a single proxy for a bunch of applications has isolation concerns that could potentially be better solved with separate processes/proxies.

Lasly, upgrading a shared proxy per node could affect all of the workloads on the node if the upgrade has issues such as version conflicts, configuration conflicts, or extension incompatibilities. Any time shared infrastructure handling application requests is upgraded, care must be taken. On the plus side, upgrading a shared-node proxy does not have to account for any of the complexities of injecting a sidecar.

From an upgrade standpoint, we can update the L7 proxy transparently to the application, however we now have more moving pieces. We need to also coordinate the upgrade of the uProxy which has some of the same drawbacks as the sidecar architecture we discussed as the first pattern.

As organizations adopt microservices, a basic part of the architecture is a network layer 7 (application layer) proxy. In large microservices environments, L7 proxies provide observability, resiliency, and routing, making it transparent to external services that they are accessing a large network of microservices.

The Envoy Proxy is an open source, high-performance, small-footprint edge and service proxy. It works similarly to software load balancers like NGINX and HAProxy. It was originally created by Lyft, and is now a large open source project with an active base of contributors. The project has been adopted by the Cloud Native Computing Foundation (CNCF) and is now at Graduated project maturity.

An Envoy Proxy is an L3/L4 proxy with a list of filters, which can connect and enable different TCP/UDP proxy processes. Additionally, it supports HTTP L7 filters since HTTP is crucial for cloud-native applications and TLS termination. It has advanced load balancing functions like circuit breaking and auto-retry, and can route gPRC requests and responses. Its configuration is manageable through an API that can push updates dynamically, even while the cluster is running.

In addition to configuring the nodes, the CNI plugin also configures the iptables for the ztunnel proxy. I already briefly mentioned and introduced the pistioin and pistioout interfaces. These interfaces on the ztunnel side are connected to the istioin and istioout interfaces on the node using the GENEVE tunnels.

The waypoint proxies are deployed per service account and can live on any node, regardless of where the actual workload is running. When the waypoint is deployed, the ztunnel configuration is updated and any workloads whose traffic should be handled through the waypoint proxy (i.e. workloads using the same service account) have a waypoint address added to their configuration entry.

For example, if we have two workloads, both with waypoint proxies deployed, and make a request from workload A to workload B, the request will skip the client-side (workload A) waypoint proxy, and end up on the server-side (waypoint proxy B) and then on the workload B.

Over the last year and a half our team here at Google has been working on adding dynamicextensibility to the Envoy proxy using WebAssembly. We are delighted toshare that work with the world today, as well asunveiling WebAssembly (Wasm) for Proxies (Proxy-Wasm): an ABI,which we intend to standardize; SDKs; and its first major implementation, the new,lower-latency Istio telemetry system.

The need for extensibility has been a founding tenet of both the Istio and Envoy projects,but the two projects took different approaches. Istio project focused on enabling a genericout-of-process extension model called Mixerwith a lightweight developer experience, while Envoy focused on in-proxy extensions.

In this example, the application looks up a Kubernetes Service FQDN with a DNS call (in this case, Istio can proxy DNS locally to save load on the Kubernetes DNS servers as well as implement zone-aware load balancing, et al.) and then takes the Cluster IP to make a call. This call goes through the Istio sidecar proxy which then gets matched to the appropriate upstream endpoint. When the traffic leaves the Pod, all of the routing and load balancing decisions have already been made and the call will be to an actual Pod IP address. Kube-proxy does not need to do anything in this case.

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