Queues int32 `json:"queues" protobuf:"varint,1,opt,name=queues"`

	// `handSize` is a small positive number that configures the
	// shuffle sharding of requests into queues.  When enqueuing a request
	// at this priority level the request's flow identifier (a string
	// pair) is hashed and the hash value is used to shuffle the list
	// of queues and deal a hand of the size specified here.  The
	// request is put into one of the shortest queues in that hand.

	VMOVDQU		state1, (1*16)(digestPtr)

done:
	RET

// shuffle byte order from LE to BE
DATA flip_mask<>+0x00(SB)/8, $0x0405060700010203
DATA flip_mask<>+0x08(SB)/8, $0x0c0d0e0f08090a0b
DATA flip_mask<>+0x10(SB)/8, $0x0405060700010203
DATA flip_mask<>+0x18(SB)/8, $0x0c0d0e0f08090a0b
GLOBL flip_mask<>(SB), 8, $32

// shuffle xBxA -> 00BA
DATA shuff_00BA<>+0x00(SB)/8, $0x0b0a090803020100

			NextCycle: oldCache.Info.NextCycle,
		},
		Cache: make(map[string]dataUsageEntry, len(oldCache.Cache)),
	}

	// Put all buckets into channel.
	bucketCh := make(chan BucketInfo, len(buckets))

	// Shuffle buckets to ensure total randomness of buckets, being scanned.
	// Otherwise same set of buckets get scanned across erasure sets always.
	// at any given point in time. This allows different buckets to be scanned

	}
	fakePodControl.Clear()
}

// shuffle returns a new shuffled list of container controllers.
func shuffle(controllers []*apps.ReplicaSet) []*apps.ReplicaSet {
	numControllers := len(controllers)
	randIndexes := rand.Perm(numControllers)
	shuffled := make([]*apps.ReplicaSet, numControllers)
	for i := 0; i < numControllers; i++ {
		shuffled[i] = controllers[randIndexes[i]]
	}
	return shuffled
}

	    the Go tree can run a sanity check but not spend time running
	    exhaustive tests.

	-shuffle off,on,N
	    Randomize the execution order of tests and benchmarks.
	    It is off by default. If -shuffle is set to on, then it will seed
	    the randomizer using the system clock. If -shuffle is set to an
	    integer N, then N will be used as the seed value. In both cases,

      misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);
    sampleData = sampleDataList.toArray(new String[sampleDataList.size()]);
  }

  // Since we can't pass a concrete SomeEnum.class directly, we need to use a raw type.
  @SuppressWarnings("unchecked")

      misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);
    sampleData = sampleDataList.toArray(new String[sampleDataList.size()]);
  }

  // Since we can't pass a concrete SomeEnum.class directly, we need to use a raw type.
  @SuppressWarnings("unchecked")

	if queuing.QueueLengthLimit <= 0 {
		allErrs = append(allErrs, field.Invalid(fldPath.Child("queueLengthLimit"), queuing.QueueLengthLimit, "must be positive"))
	}

	// validate input arguments for shuffle-sharding
	if queuing.Queues <= 0 {
		allErrs = append(allErrs, field.Invalid(fldPath.Child("queues"), queuing.Queues, "must be positive"))
	} else if queuing.Queues > priorityLevelConfigurationQueuingMaxQueues {

		// Run for a number of times, shuffling the input to ensure that output is consistent.
		for i := 0; i < 10; i++ {
			t.Run(fmt.Sprintf("test-%d-%s-run-%d", testID, tt.name, i), func(t *testing.T) {
				if i > 0 {
					rng.Shuffle(len(tt.m), func(i, j int) {
						tt.m[i], tt.m[j] = tt.m[j], tt.m[i]
					})
				}
				gotSelected, gotOk := tt.m.resolve(&tt.r)
				if gotOk != tt.wantOk {

 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the
 * data using an unpredictable seed should normally be enough to thwart this attack.
 *
 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes