if v.N > 0 {
					di.Metrics.LastMinute[k] = v.asTimedAction()
				}
			}
			for k, v := range info.Metrics.APICalls {
				di.Metrics.APICalls[k] = v
			}
			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.

	// 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

## 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