// targetAverageUtilization is the target value of the average of the
  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.
  // +optional
  optional int32 targetAverageUtilization = 2;

  // targetAverageValue is the target value of the average of the
  // resource metric across all relevant pods, as a raw value (instead of as

    }

    // Check that asterisk did not match across domain name labels.
    val suffixStartIndexInHostname = hostname.length - suffix.length
    if (suffixStartIndexInHostname > 0 &&
      hostname.lastIndexOf('.', suffixStartIndexInHostname - 1) != -1
    ) {
      return false // Asterisk is matching across domain name labels -- not permitted.
    }

    // Hostname matches pattern.

system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas...

  // targetAverageUtilization is the target value of the average of the
  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.
  // +optional
  optional int32 targetAverageUtilization = 2;

  // targetAverageValue is the target value of the average of the
  // resource metric across all relevant pods, as a raw value (instead of as

  // averageValue is the target value of the average of the
  // metric across all relevant pods (as a quantity)
  // +optional
  optional k8s.io.apimachinery.pkg.api.resource.Quantity averageValue = 3;

  // averageUtilization is the target value of the average of the
  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.

microservices](https://istio.io/latest/docs/examples/microservices-istio/), manage [traffic flow](https://istio.io/latest/docs/concepts/traffic-management/) across microservices, enforce policies
and aggregate telemetry data. Istio's control plane provides an abstraction
layer over the underlying cluster management platform, such as Kubernetes.

Istio is composed of these components:

For example, the `demo` profile offers a preset configuration to try out Istio in a test environment, with additional features enabled and lowered resource requirements.

For consistency, the same profiles are used across each chart, even if they do not impact a given chart.

Explicitly set values have highest priority, then profile settings, then chart defaults.

## Prometheus Probe

MinIO allows reading metrics for the entire cluster from any single node. This allows for metrics collection for a MinIO instance across all servers. Thus, metrics collection for instances behind a load balancer can be done without any knowledge of the individual node addresses. The cluster wide metrics can be read at
`<Address for MinIO Service>/minio/v2/metrics/cluster`.

# ADR-0002 - Avoid using Java serialization

## Date

2012-12-01

## Context

In Gradle we often need to serialize in-memory objects for caching, or to transmit them across process barriers, etc.
Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.

Your code needs to run on [all versions of Java that Gradle supports](platforms/documentation/docs/src/docs/userguide/releases/compatibility.adoc) and across all supported operating systems (macOS, Windows, Linux). The [Gradle CI system](http://builds.gradle.org/) will verify this, but here are some pointers that will avoid surprises: