Cloud Service Mesh load balancing
Cloud Service Mesh uses sidecar proxies or proxyless gRPC to deliver global load balancing for your internal microservices. You can deploy internal microservices (sidecar proxy-based or proxyless gRPC-based) with instances in multiple regions. Cloud Service Mesh provides health, routing, and backend information to the sidecar proxies or proxyless gRPC, enabling them to perform optimal traffic routing to application instances in multiple cloud regions for a service.
In the following diagram, user traffic enters a Google Cloud deployment through an external global load balancer. The external load balancer distributes traffic to the Front End microservice in either us-central1 or asia-southeast1, depending on the location of the end user.
The internal deployment features three global microservices: Front End, Shopping Cart, and Payments. Each service runs on managed instance groups (MIGs) in two regions, us-central1 and asia-southeast1. Cloud Service Mesh uses a global load-balancing algorithm that directs traffic from the user in California to the microservices deployed in us-central1. Requests from the user in Singapore are directed to the microservices in asia-southeast1.
An incoming user request is routed to the Front End microservice. The service proxy installed on the host with the Front End then directs traffic to the Shopping Cart. The sidecar proxy installed on the host with the Shopping Cart directs traffic to the Payments microservice. In a proxyless gRPC environment, your gRPC application would handle traffic management.
In the following example, if Cloud Service Mesh receives health check results that indicate that the virtual machine (VM) instances running the Shopping Cart microservice in us-central1 are unhealthy, Cloud Service Mesh instructs the sidecar proxy for the Front End microservices to fail over traffic to the Shopping Cart microservice running in asia-southeast1. Because autoscaling is integrated with traffic management in Google Cloud, Cloud Service Mesh notifies the MIG in asia-southeast1 of the additional traffic, and the MIG increases in size.
Cloud Service Mesh detects that all backends of the Payments microservice are healthy, so Cloud Service Mesh instructs Envoy's proxy for the Shopping Cart to send a portion of the traffic—up to the customer's configured capacity—to asia-southeast1 and overflow the rest to us-central1.
Load-balancing components in Cloud Service Mesh
During Cloud Service Mesh setup, you configure several load-balancing components:
- The backend service, which contains configuration values.
- A health check, which provides health checking for the VMs and Google Kubernetes Engine (GKE) Pods in your deployment.
- With the service routing APIs, a
MeshorGatewayresource and aRouteresource. - With the load balancing APIs, a global forwarding rule, which includes the VIP address, a target proxy, and a URL map.
An xDS API-compatible sidecar proxy (such as Envoy) runs on a client VM instance or in a Kubernetes Pod. Cloud Service Mesh serves as the control plane and uses xDS APIs to communicate directly with each proxy. In the data plane, the application sends traffic to the VIP address configured in the forwarding rule or Mesh resource. The sidecar proxy or your gRPC application intercepts the traffic and redirects it to the appropriate backend.
The following diagram shows an application running on Compute Engine VMs or GKE Pods, the components, and the traffic flow in a Cloud Service Mesh deployment. It shows Cloud Service Mesh and the Cloud Load Balancing resources that are used to determine traffic routing. The diagram shows the older load balancing APIs.
What's next
- To learn about configuring advance load balancing features, see Advanced load balancing overview.
- To learn more about service discovery and traffic interception, see Cloud Service Mesh service discovery.
- To learn more about Cloud Service Mesh with the service routing APIs, see the overview.