int maximumSize;

  @Param("5000")
  int distinctKeys;

  @Param("4")
  int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);

package bandwidth

//go:generate msgp -file=$GOFILE -unexported

import (
	"context"
	"slices"
	"sync"
	"time"

	"golang.org/x/time/rate"
)

//msgp:ignore bucketThrottle Monitor

type bucketThrottle struct {
	*rate.Limiter
	NodeBandwidthPerSec int64
}

// Monitor holds the state of the global bucket monitor
type Monitor struct {
	tlock sync.RWMutex // mutex for bucket throttling

 *       thoroughly as possible.
 *   <li><b>bit-dispersing:</b> masking out any <i>single bit</i> from a hash code should yield only
 *       the expected <i>twofold</i> increase to all collision rates. Informally, the "information"
 *       in the hash code should be as evenly "spread out" through the hash code's bits as possible.
 *       The result is that, for example, when choosing a bucket in a hash table of size 2^8,

 *       thoroughly as possible.
 *   <li><b>bit-dispersing:</b> masking out any <i>single bit</i> from a hash code should yield only
 *       the expected <i>twofold</i> increase to all collision rates. Informally, the "information"
 *       in the hash code should be as evenly "spread out" through the hash code's bits as possible.
 *       The result is that, for example, when choosing a bucket in a hash table of size 2^8,

    }

    /**
     * Increment error counter
     */
    public void incrementErrors() {
        errors.incrementAndGet();
    }

    /**
     * Get error rate (errors / total requests)
     *
     * @return error rate between 0.0 and 1.0
     */
    public double getErrorRate() {
        long total = requestsSent.get();
        if (total == 0)
            return 0.0;

  int maximumSize;

  @Param("5000")
  int distinctKeys;

  @Param("4")
  int segments;

  // 1 means uniform likelihood of keys; higher means some keys are more popular
  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);

            long avgResponseTime = metrics.getAverageResponseTime();

            // Adjust threshold based on failure rate and response time trends
            int currentThreshold = this.failureThreshold;
            int newThreshold = currentThreshold;

            // If failure rate is high but response time is normal, be more lenient
            if (failureRate > 0.5 && avgResponseTime < 5000) {

└─────┴─────────────────────────────────┴──────────────────────────────────┴────────┘
```

### Decommissioning status

```
λ mc admin decommission status alias/ http://minio{1...2}/data{1...4}
Decommissioning rate at 36 MiB/sec [4 TiB/50 TiB]
Started: 1 minute ago
```

Once it is **Complete**

```
λ mc admin decommission status alias/ http://minio{1...2}/data{1...4}

    }

    /**
     * Configure rate limiting
     *
     * @param windowMs time window in milliseconds
     * @param maxEvents maximum events per window
     */
    public void setRateLimit(long windowMs, int maxEvents) {
        this.rateLimitWindow = windowMs;
        this.maxEventsPerWindow = maxEvents;
        log.info("Rate limiting set to {} events per {} ms", maxEvents, windowMs);
    }

        assertTrue(primaryScore > baseScore);

        // High error rate should reduce score
        channelInfo.setState(ChannelState.ESTABLISHED);
        channelInfo.setPrimary(false);
        for (int i = 0; i < 20; i++) {
            channelInfo.incrementRequestsSent();
            channelInfo.incrementErrors(); // 100% error rate
        }
        int highErrorScore = channelInfo.getScore();