// GetDiskFreeSpaceEx - https://msdn.microsoft.com/en-us/library/windows/desktop/aa364937(v=vs.85).aspx
	// Retrieves information about the amount of space that is available on a disk volume,
	// which is the total amount of space, the total amount of free space, and the total
	// amount of free space available to the user that is associated with the calling thread.
	GetDiskFreeSpaceEx = kernel32.NewProc("GetDiskFreeSpaceExW")

      return state.policy.backoffDelayMillis.jitterBy(state.policy.backoffJitterMillis) * 1_000_000
    }
  }

  private fun Long.jitterBy(amount: Int): Long {
    return this + ThreadLocalRandom.current().nextInt(amount * -1, amount)
  }

  class AddressState(
    val address: Address,
    val queue: TaskQueue,
    var policy: ConnectionPool.AddressPolicy,
  ) {
    /**

            final byte[] buf = buffer.array();
            int len;
            int amount = 0;
            while ((len = ChannelUtil.read(channel, buffer, amount)) != -1) {
                out.write(buf, 0, len);
                buffer.clear();
                amount += len;
            }
            out.flush();
            return amount;
        } catch (final IOException e) {
            throw new IORuntimeException(e);

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.

  }

  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()
  }

		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('[')

  @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.
   *

    * So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all...

const (
	// browserCSPPolicy setting name for Content-Security-Policy response header value
	browserCSPPolicy = "csp_policy"
	// browserHSTSSeconds setting name for Strict-Transport-Security response header, amount of seconds for 'max-age'
	browserHSTSSeconds = "hsts_seconds"
	// browserHSTSIncludeSubdomains setting name for Strict-Transport-Security response header 'includeSubDomains' flag (true or false)

	// if TTL has expired but 2x TTL has not yet passed,
	// but will fetch a new value in the background.
	NoWait bool
}

// Cache contains a synchronized value that is considered valid
// for a specific amount of time.
// An Update function must be set to provide an updated value when needed.
type Cache[T any] struct {
	// updateFn must return an updated value.
	// If an error is returned the cached value is not set.