token: Bearer xxxxx # optional authentication token for the notification endpoint
  
  retry:
    attempts: 10 # number of retries for the job before giving up
    delay: 500ms # least amount of delay between each retry
`
	var job BatchJobRequest
	err := yaml.Unmarshal([]byte(expireYaml), &job)
	if err != nil {
		t.Fatal("Failed to parse batch-job-expire yaml", err)
	}

  }

  private fun updateMaxConcurrentStreams(
    connection: Http2Connection,
    amount: Int,
  ) {
    val settings = Settings()
    settings[Settings.MAX_CONCURRENT_STREAMS] = amount
    connection.readerRunnable.applyAndAckSettings(true, settings)
    assertThat(connection.peerSettings[Settings.MAX_CONCURRENT_STREAMS]).isEqualTo(amount)
    taskFaker.runTasks()
  }

#      token: "Bearer xxxxx" # optional authentication token for the notification endpoint
#
#    retry:
#      attempts: 10 # number of retries for the job before giving up
#      delay: "500ms" # least amount of delay between each retry

`
	var job BatchJobRequest
	err := yaml.Unmarshal([]byte(replicateYaml), &job)
	if err != nil {
		t.Fatal("Failed to parse batch-job-replicate yaml", err)
	}

  }

  /**
   * Sends the given method call to this thread.
   *
   * @throws TimeoutException if this thread does not accept the request within a reasonable amount
   *     of time
   */
  private void sendRequest(String methodName, Object... arguments) throws Exception {
    if (!requestQueue.offer(new Request(methodName, arguments), TIMEOUT_MILLIS, MILLISECONDS)) {

  @CanIgnoreReturnValue
  public FakeTicker setAutoIncrementStep(long autoIncrementStep, TimeUnit timeUnit) {
    checkArgument(autoIncrementStep >= 0, "May not auto-increment by a negative amount");
    this.autoIncrementStepNanos = timeUnit.toNanos(autoIncrementStep);
    return this;
  }

  /**
   * Sets the increment applied to the ticker whenever it is queried.
   *

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

### Examples of Replication Tools and Strategies

There can be several approaches to achieve this, and I'll tell you more about specific strategies in the next chapters, for example when talking about Docker and containers.

    list.add(new MockRunnable(countDownLatch), exec);
    assertEquals(3L, countDownLatch.getCount());

    list.execute();

    // Verify that all of the runnables execute in a reasonable amount of time.
    assertTrue(countDownLatch.await(1L, SECONDS));
  }

  public void testExecute_idempotent() {
    final AtomicInteger runCalled = new AtomicInteger();
    list.add(
        new Runnable() {

     */
    private final MavenProject project;

    /**
     * The build time of the project in milliseconds.
     */
    private final long wallTime;

    /**
     * The total amount of time spent for to run mojos in milliseconds.
     */
    private final long execTime;

    /**
     * Creates a new build summary for the specified project.
     *

		tok := p.next()
		ext = tok.String()
	}
	if p.peek() == lex.LSH {
		// parses left shift amount applied after extension: <<Amount
		p.get(lex.LSH)
		tok := p.get(scanner.Int)
		amount, err := strconv.ParseInt(tok.String(), 10, 16)
		if err != nil {
			p.errorf("parsing left shift amount: %s", err)
		}
		num = int16(amount)
	} else if p.peek() == '[' {
		// parses an element: [Index]
		p.get('[')

you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

And then you can set those same memory limits and requirements in your configurations for your container management system (for example in **Kubernetes**). That way it will be able to **replicate the containers** in the **available machines** taking into account the amount of memory needed by them, and the amount available in the machines...