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,
  ) {
    /**

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()
  }

    * 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)

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...

//     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

//go:generate msgp -file $GOFILE -unexported

// BatchKeyRotationType defines key rotation type
type BatchKeyRotationType string

const (
	sses3  BatchKeyRotationType = "sse-s3"

	}
	return
}

// Joins specify Joins conditions
//
//	db.Joins("Account").Find(&user)
//	db.Joins("JOIN emails ON emails.user_id = users.id AND emails.email = ?", "******@****.***").Find(&user)
//	db.Joins("Account", DB.Select("id").Where("user_id = users.id AND name = ?", "someName").Model(&Account{}))
func (db *DB) Joins(query string, args ...interface{}) (tx *DB) {

     * values must be between 1 and [Integer.MAX_VALUE] when converted to milliseconds.
     *
     * The close timeout is the maximum amount of time after the client calls [WebSocket.close] to
     * wait for a graceful shutdown. If the server doesn't respond the web socket will be canceled.
     * The default value is 60 seconds.
     */

		Value:  xhttp.DefaultIdleTimeout,
		Usage:  "idle timeout is the maximum amount of time to wait for the next request when keep-alive are enabled",
		EnvVar: "MINIO_IDLE_TIMEOUT",
		Hidden: true,
	},
	cli.DurationFlag{
		Name:   "read-header-timeout",
		Value:  xhttp.DefaultReadHeaderTimeout,
		Usage:  "read header timeout is the amount of time allowed to read request headers",
		EnvVar: "MINIO_READ_HEADER_TIMEOUT",