* update expose command description to add deployment ([#23246](https://github.com/kubernetes/kubernetes/pull/23246), [@AdoHe](https://github.com/AdoHe))
* Add a rate limiter to the GCE cloudprovider ([#23019](https://github.com/kubernetes/kubernetes/pull/23019), [@alex-mohr](https://github.com/alex-mohr))

Rabbit with pink eyes ran close by her.

  There was nothing so VERY remarkable in that; nor did Alice
think it so VERY much out of the way to hear the Rabbit say to
itself, `Oh dear!  Oh dear!  I shall be late!'  (when she thought
it over afterwards, it occurred to her that she ought to have
wondered at this, but at the time it all seemed quite natural);
but when the Rabbit actually TOOK A WATCH OUT OF ITS WAISTCOAT-

Rabbit with pink eyes ran close by her.

  There was nothing so VERY remarkable in that; nor did Alice
think it so VERY much out of the way to hear the Rabbit say to
itself, `Oh dear!  Oh dear!  I shall be late!'  (when she thought
it over afterwards, it occurred to her that she ought to have
wondered at this, but at the time it all seemed quite natural);
but when the Rabbit actually TOOK A WATCH OUT OF ITS WAISTCOAT-

 * the updated response if it has changed, or a short 'not modified' response if the client's copy
 * is still valid. Such responses increment both the network count and hit count.
 *
 * The best way to improve the cache hit rate is by configuring the web server to return cacheable
 * responses. Although this client honors all [HTTP/1.1 (RFC 7234)][rfc_7234] cache headers, it
 * doesn't cache partial responses.
 *
 * ## Force a Network Response
 *

    Closer closer = Closer.create();
    try {
      InputStream in = closer.register(openStream());
      return countBySkipping(in);
    } catch (IOException e) {
      // skip may not be supported... at any rate, try reading
    } finally {
      closer.close();
    }

    closer = Closer.create();
    try {
      InputStream in = closer.register(openStream());
      return ByteStreams.exhaust(in);

      },
      "pluginVersion": "10.1.5",
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "${datasource}"
          },
          "expr": "sum(rate(container_cpu_usage_seconds_total{container=\"istio-proxy\"}[1m]))",
          "format": "time_series",
          "hide": false,
          "intervalFactor": 2,
          "legendFormat": "Total (k8s)",

}

// SlowDown  too many file descriptors open or backend busy .
type SlowDown struct{}

func (e SlowDown) Error() string {
	return "Please reduce your request rate"
}

// RQErrType reason for read quorum error.
type RQErrType int

const (
	// RQInsufficientOnlineDrives - not enough online drives.
	RQInsufficientOnlineDrives RQErrType = 1 << iota

          Assert.assertEquals(0.50d, prob, epsilon);
        }
      }
    }
  }

  /**
   * Checks that a Hasher returns the same HashCode when given the same input, and also that the
   * collision rate looks sane.
   */
  static void assertInvariants(HashFunction hashFunction) {
    int objects = 100;
    Set<HashCode> hashcodes = Sets.newHashSetWithExpectedSize(objects);
    Random random = new Random(314159);

   *
   * <p>See http://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives for the
   * formula.
   *
   * @param n expected insertions (must be positive)
   * @param p false positive rate (must be 0 < p < 1)
   */
  @VisibleForTesting
  static long optimalNumOfBits(long n, double p) {
    if (p == 0) {
      p = Double.MIN_VALUE;
    }

   *
   * The Least Recently Used page replacement algorithm was chosen due to its simplicity, high hit
   * rate, and ability to be implemented with O(1) time complexity. The initial LRU implementation
   * operates per-segment rather than globally for increased implementation simplicity. We expect
   * the cache hit rate to be similar to that of a global LRU algorithm.
   */

  // Constants

  /**