"vendor_id", "pickup_datetime", "dropoff_datetime", "store_and_fwd_flag", "rate_code_id", "pickup_longitude", "pickup_latitude", "dropoff_longitude", "dropoff_latitude", "passenger_count", "trip_distance", "fare_amount", "extra", "mta_tax", "tip_amount", "tolls_amount", "ehail_fee", "improvement_surcharge", "total_amount", "payment_type", "trip_type", "pickup", "dropoff", "cab_type", "precipitation", "snow_depth", "snowfall", "max_temp", "min_temp", "wind", "pickup_nyct2010_gid", "pickup_ctlabel",...

	MOVWU	(R5)(R4.UXTW<<3), R10                            // ERROR "invalid index shift amount"
	MOVWU	(R5)(R4<<1), R10                                 // ERROR "invalid index shift amount"
	MOVB	(R5)(R4.SXTW<<5), R10                            // ERROR "invalid index shift amount"
	MOVH	R5, (R6)(R2<<3)                                  // ERROR "invalid index shift amount"

      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()
  }

		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('[')

  }

  /**
   * 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(

  }

  /**
   * 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(

   * "expected arrival time of the next request" is actually in the past, then the difference (now -
   * past) is the amount of time that the RateLimiter was formally unused, and it is that amount of
   * time which we translate to storedPermits. (We increase storedPermits with the amount of permits
   * that would have been produced in that idle time). So, if rate == 1 permit per second, and

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...