Each node is responsible of healing its local drives; Each drive will have multiple heal workers which is the quarter of the number of CPU cores of the node or the quarter of the configured nr_requests of the drive (https://www.kernel.org/doc/Documentation/block/queue-sysfs.txt). It is also possible to provide a custom number of workers by using this command: `mc admin config set alias/ heal drive_workers=100` .


```
~ mc admin config set alias/ heal

Care should be taken when using vector instructions to ensure that they are available at
runtime.
To use vector instructions a machine must have both the vector facility (bit 129 in the
facility list) and kernel support.
Without kernel support a vector instruction will have no effect (it will be equivalent
to a <code>NOP</code> instruction).
</p>

<p>
Addressing modes:
</p>

<ul>

<li>

      [ -s "${CLUSTER_KUBECONFIG}" ] && break
      sleep 3
    done

    # Enable core dumps
    retry docker exec "${CLUSTER_NAME}"-control-plane bash -c "sysctl -w kernel.core_pattern=/var/lib/istio/data/core.proxy && ulimit -c unlimited"
  }

  # Now deploy the specified number of KinD clusters and
  # wait till they are provisioned successfully.
  declare -a DEPLOY_KIND_JOBS

Linux-Container werden mit demselben Linux-Kernel des Hosts (Maschine, virtuellen Maschine, Cloud-Servers, usw.) ausgeführt. Das bedeutet einfach, dass sie sehr leichtgewichtig sind (im Vergleich zu vollständigen virtuellen Maschinen, die ein gesamtes Betriebssystem emulieren).

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

Contêineres Linux rodam usando o mesmo kernel Linux do hospedeiro (máquina, máquina virtual, servidor na nuvem, etc). Isso simplesmente significa que eles são muito leves (comparados com máquinas virtuais emulando um sistema operacional completo).

TF_CAPI_EXPORT extern bool TFE_ExecutorIsAsync(TFE_Executor*);

// Causes the calling thread to block till all ops dispatched in this executor
// have been executed. Note that "execution" here refers to kernel execution /
// scheduling of copies, etc. Similar to sync execution, it doesn't guarantee
// that lower level device queues (like GPU streams) have been flushed.
//

		Name:      readTotal,
		Help:      "Total read SysCalls to the kernel. /proc/[pid]/io syscr",
		Type:      counterMetric,
	}
}

func getMinIOProcessSysCallWMD() MetricDescription {
	return MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: sysCallSubsystem,
		Name:      writeTotal,
		Help:      "Total write SysCalls to the kernel. /proc/[pid]/io syscw",
		Type:      counterMetric,
	}
}

- Faster mount detection for linux kernel 5.10+ using openat2 speeding up pod churn rates. On Kernel versions less 5.10, it will fallback to using the original way of detecting mount points i.e by parsing /proc/mounts. ([#109217](https://github.com/kubernetes/kubernetes/pull/109217), [@manugupt1](https://github.com/manugupt1))...

            "//tensorflow/core/common_runtime/eager:eager_operation",
            "//tensorflow/core/common_runtime/eager:execute",
            "//tensorflow/core/common_runtime/eager:kernel_and_device",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/lib/llvm_rtti",
            "@com_google_absl//absl/types:variant",
        ],
    }) + select({