// acceptable quorum expected for list operations
func getListQuorum(quorum string, driveCount int) int {
	switch quorum {
	case "disk":
		return 1
	case "reduced":
		return 2
	case "optimal":
		return (driveCount + 1) / 2
	case "auto":
		return -1
	}
	// defaults to 'strict'
	return driveCount
}

	}
	if buffered := wr.Buffered(); buffered != wantBuffered {
		t.Fatalf("Buffered = %v; want %v", buffered, wantBuffered)
	}
}

func BenchmarkReaderCopyOptimal(b *testing.B) {
	// Optimal case is where the underlying reader implements io.WriterTo
	srcBuf := bytes.NewBuffer(make([]byte, 8192))
	src := NewReader(srcBuf)
	dstBuf := new(bytes.Buffer)
	dst := onlyWriter{dstBuf}
	for i := 0; i < b.N; i++ {

	maxProcs := runtime.GOMAXPROCS(0)
	cpuProcs := runtime.NumCPU()
	if maxProcs < cpuProcs {
		warnings = append(warnings, color.YellowBold("Detected GOMAXPROCS(%d) < NumCPU(%d), please make sure to provide all PROCS to MinIO for optimal performance",
			maxProcs, cpuProcs))
	}

	// Initialize grid
	bootstrapTrace("initGrid", func() {
		logger.FatalIf(initGlobalGrid(GlobalContext, globalEndpoints), "Unable to configure server grid RPC services")

	// 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

	smallFileThreshold = 128 * humanize.KiByte // Optimized for NVMe/SSDs

	// For hardrives it is possible to set this to a lower value to avoid any
	// spike in latency. But currently we are simply keeping it optimal for SSDs.

	// bigFileThreshold is the point where we add readahead to put operations.
	bigFileThreshold = 128 * humanize.MiByte

	// XL metadata file carries per object metadata.
	xlStorageFormatFile = "xl.meta"

  pods` preferred inter-pod affinities if the incoming pod has no preferred inter-pod
  affinities. This option can be used as an optimization for higher scheduling throughput
  (at the cost of an occasional pod being scheduled non-optimally/violating existing
  pods preferred inter-pod affinities). To enable this scheduler option, set the

    *   Added APIs for switching between interactive logging and absl logging.
        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL

hash.Size()+1] db := em[hash.Size()+1:] mgf1XOR(seed, mgfHash, db) mgf1XOR(db, mgfHash, seed) lHash2 := db[0:hash.Size()] // We have to validate the plaintext in constant time in order to avoid // attacks like: J. Manger. A Chosen Ciphertext Attack on RSA Optimal // Asymmetric Encryption Padding (OAEP) as Standardized in PKCS #1 // v2.0. In J. Kilian, editor, Advances in Cryptology. lHash2Good := subtle.ConstantTimeCompare(lHash, lHash2) // The remainder of the plaintext must be zero or more 0x00, followed...

hash.Size()+1] db := em[hash.Size()+1:] mgf1XOR(seed, mgfHash, db) mgf1XOR(db, mgfHash, seed) lHash2 := db[0:hash.Size()] // We have to validate the plaintext in constant time in order to avoid // attacks like: J. Manger. A Chosen Ciphertext Attack on RSA Optimal // Asymmetric Encryption Padding (OAEP) as Standardized in PKCS #1 // v2.0. In J. Kilian, editor, Advances in Cryptology. lHash2Good := subtle.ConstantTimeCompare(lHash, lHash2) // The remainder of the plaintext must be zero or more 0x00, followed...