[GitHub] [helix] i3wangyi commented on a change in pull request #520: Refactor soft constraints to simply the algorithm and fix potential issues.

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i3wangyi commented on a change in pull request #520: Refactor soft constraints to simply the algorithm and fix potential issues.
URL: https://github.com/apache/helix/pull/520#discussion_r338811060
 
 

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 File path: helix-core/src/main/java/org/apache/helix/controller/rebalancer/waged/constraints/UsageSoftConstraint.java
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 @@ -0,0 +1,74 @@
+package org.apache.helix.controller.rebalancer.waged.constraints;
+
+    /*
+     * Licensed to the Apache Software Foundation (ASF) under one
+     * or more contributor license agreements.  See the NOTICE file
+     * distributed with this work for additional information
+     * regarding copyright ownership.  The ASF licenses this file
+     * to you under the Apache License, Version 2.0 (the
+     * "License"); you may not use this file except in compliance
+     * with the License.  You may obtain a copy of the License at
+     *
+     *   http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing,
+     * software distributed under the License is distributed on an
+     * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+     * KIND, either express or implied.  See the License for the
+     * specific language governing permissions and limitations
+     * under the License.
+     */
+
+    import org.apache.commons.math3.analysis.function.Sigmoid;
+
+/**
+ * The soft constraint that evaluates the assignment proposal based on usage.
+ */
+abstract class UsageSoftConstraint extends SoftConstraint {
+  private static final double MAX_SCORE = 1f;
+  private static final double MIN_SCORE = 0f;
+  /**
+   * Alpha is used to adjust how smooth the sigmoid function should be.
+   * As tested, when we have the input number which surrounding 1, the default alpha value will
+   * ensure a smooth curve (sigmoid(0.95) = 0.90, sigmoid(1.05) = 0.1).
+   * This means if the usage is within +-5% difference compared with the estimated usage, the
+   * evaluated score will be reasonably different so the rebalancer can decide accordingly.
+   * Else, if the current usage is much less or more than the estimation, the score will be very
+   * close to 1.0 (less than estimation), or very close to 0.1 (more than estimation).
+   **/
+  private static final int DEFAULT_ALPHA = 44;
+  private static final Sigmoid SIGMOID = new Sigmoid();
+
+  UsageSoftConstraint() {
+    super(MAX_SCORE, MIN_SCORE);
+  }
+
+  /**
+   * Compute utilization score based on the current usage and the estimated usage.
+   * The score is evaluated using a sigmoid function.
+   * When the usage is smaller than estimation, the constraint returns a value that is very close to
+   * the max score.
+   * When the usage is close or larger than the estimate, the constraint returns a score that is
+   * very close to the min score. Note even in this case, more usage will still be assigned with a
+   * smaller score.
+   * @param estimatedUsage The estimated usage that is between [0.0, 1.0]
+   * @param currentUsage The current usage that is between [0.0, 1.0]
+   * @return The score between [0.0, 1.0] that evaluates the utilization.
+   */
+  protected double computeUtilizationScore(double estimatedUsage, double currentUsage) {
+    if (estimatedUsage == 0) {
+      return 0;
+    }
+    return SIGMOID.value(-(currentUsage / estimatedUsage - 1) * DEFAULT_ALPHA) * (MAX_SCORE
 
 Review comment:
   Sigmoid function is not complicated in Java. Let's use 
   `1 / (1 + Math.exp(X))`, I've tested the result is exactly same.

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