/// tip

I'll give you more concrete examples for doing this with containers in a future chapter: [FastAPI in Containers - Docker](docker.md){.internal-link target=_blank}.

///

## Resource Utilization { #resource-utilization }

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.

## Storage usage

The selection of varying data and parity drives has a direct impact on the drive space usage. With storage class, you can optimize for high
redundancy or better drive space utilization.

To get an idea of how various combinations of data and parity drives affect the storage usage, let’s take an example of a 100 MiB file stored

     * Get current cache statistics for monitoring
     * @return formatted statistics string
     */
    public static String getCacheStatistics() {
        return String.format("BufferCache: queued=%d, max=%d, utilization=%.1f%%", queueSize.get(), MAX_BUFFERS,
                (queueSize.get() * 100.0 / MAX_BUFFERS));
    }

    /**
     * Clear all cached buffers (for testing or cleanup)
     */

				if v.N > 0 {
					di.Metrics.LastMinute[k] = v.asTimedAction()
				}
			}
			maps.Copy(di.Metrics.APICalls, info.Metrics.APICalls)
			if info.Total > 0 {
				di.Utilization = float64(info.Used / info.Total * 100)
			}
			disksInfo[index] = di
			return nil
		}, index)
	}

	g.Wait()
	return disksInfo
}

// Get an aggregated storage info across all disks.

                            + "healthChecks=%d, failed=%d, removed=%d, endpoints=%d, "
                            + "healthCheckEnabled=%s, proactiveEnabled=%s, utilization=%.2f%%, successRate=%.2f%%]",
                    activeConnections, nonPooledConnections, trackedConnections, maxPoolSize, totalHealthChecks, failedHealthChecks,

	// in the 10 node shard would coordinate between other 9 shards to get the
	// rest of the `99*9` requests.
	//
	// This essentially splits the workload properly and also allows for network
	// utilization to be optimal, instead of blindly throttling the way we are
	// doing below. However the changes that are needed here are a bit involved,
	// further discussion advised. Remove this comment and remove the worker model

- DRA: scheduling pods is up to 16x faster, depending on the scenario. Scheduling throughput depends a lot on cluster utilization. It is higher for lightly loaded clusters with free resources and gets lower when the cluster utilization increases. ([#127277](https://github.com/kubernetes/kubernetes/pull/127277), [@pohly](https://github.com/pohly)) [SIG API Machinery, Apps, Architecture, Auth, Etcd, Instrumentation, Node, Scheduling...

    Detection using Deep Neural Networks.
*   New (experimental)
    [Java API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java).
*   Add new Android image stylization demo based on "A Learned Representation
    For Artistic Style", and add YOLO object detector support.

## Breaking Changes to the API