///

## Utilização de recursos

Seu(s) servidor(es) é(são) um **recurso** que você pode consumir ou **utilizar**, com seus programas, o tempo de computação nas CPUs e a memória RAM disponível.

Quanto dos recursos do sistema você quer consumir/utilizar? Pode ser fácil pensar "não muito", mas, na realidade, você provavelmente vai querer consumir **o máximo possível sem travar**.

      // otherwise have another task queued on to it. Note the exception to this, cancellation, is
      // specially handled in execute() - execute() calls triggered by cancellation are no-ops, and
      // thus don't count.
      requireNonNull(sequencer).latestTaskQueue = executingTaskQueue;
      sequencer = null;
      try {
        // requireNonNull is safe, as discussed above.

| Path                        | Description                                        |
|-----------------------------|----------------------------------------------------|
| `/system/cpu`               | Metrics about CPUs on the system.                  |
| `/system/drive`             | Metrics about drives on the system.                |
| `/system/network/internode` | Metrics about internode requests made by the node. |

- When the kubelet fails to assign CPUs to a Pod because there less available CPUs than the Pod requests, the error message changed from

   char *s = s1;
 
   /* Find the end of S1.  */
-  do
-    c = *s1++;
-  while (c != '\0');
-
-  /* Make S1 point before next character, so we can increment
-     it while memory is read (wins on pipelined cpus).  */
-  s1 -= 2;
+  s1 += strlen (s1);
 
-  if (n >= 4)
-    {
-      size_t n4 = n >> 2;
-      do
-	{
-	  c = *s2++;
-	  *++s1 = c;
-	  if (c == '\0')
-	    return s;

* Use local disk for ConfigMap volume instead of tmpfs ([#25306](https://github.com/kubernetes/kubernetes/pull/25306), [@pmorie](https://github.com/pmorie))
* Alpha support for scheduling pods on machines with NVIDIA GPUs whose kubelets use the `--experimental-nvidia-gpus` flag, using the alpha.kubernetes.io/nvidia-gpu resource  ([#24836](https://github.com/kubernetes/kubernetes/pull/24836), [@therc](https://github.com/therc))

	VREV32	V5.B16, V5.B16                  // a508206e
	VREV64	V2.S2, V3.S2                    // 4308a00e
	VREV64	V2.S4, V3.S4                    // 4308a04e

// logical ops
//
// make sure constants get encoded into an instruction when it could
	AND	R1@>33, R2
	AND	$(1<<63), R1                        // AND	$-9223372036854775808, R1       // 21004192

            // Some operations might succeed before key wipe, some might fail after
            // At minimum, we expect some activity (not all operations should be no-ops)
            int totalOperations = successCount.get() + failureCount.get();
            assertTrue(totalOperations > 0, "Some signing/verification operations should have been attempted");
        }

        @Test

</p>

<p>
Note that the prohibition on introducing data races
does not apply if the compiler can prove that the races
do not affect correct execution on the target platform.
For example, on essentially all CPUs, it is valid to rewrite
</p>

<pre>
n := 0
for i := 0; i < m; i++ {
	n += *shared
}
</pre>

into:

<pre>
n := 0
local := *shared
for i := 0; i < m; i++ {
	n += local

  * Create clusters with GPUs in GCE by specifying `type=<gpu-type>,count=<gpu-count>` to NODE_ACCELERATORS environment variable. ([#45130](https://github.com/kubernetes/kubernetes/pull/45130), [@vishh](https://github.com/vishh))

    * List of available GPUs - [https://cloud.google.com/compute/docs/gpus/#introduction](https://cloud.google.com/compute/docs/gpus/#introduction)