Introducing the FullNode class, which keeps track of the delay to each full node it represents

This commit is contained in:
Nelson R. Perez 2018-09-17 17:48:49 -05:00
parent 56fb257eb2
commit 83ce2de14d
3 changed files with 143 additions and 0 deletions

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package cy.agorise.graphenej.network;
import cy.agorise.graphenej.stats.ExponentialMovingAverage;
/**
* Class that represents a full node and is used to keep track of its round-trip time measured in milliseconds.
*/
public class FullNode implements Comparable {
private String mUrl;
private ExponentialMovingAverage latency;
private FullNode(){}
public FullNode(String url){
latency = new ExponentialMovingAverage(ExponentialMovingAverage.DEFAULT_ALPHA);
this.mUrl = url;
}
/**
* Full node URL getter
* @return
*/
public String getUrl() {
return mUrl;
}
/**
* Full node URL setter
* @param mUrl
*/
public void setUrl(String mUrl) {
this.mUrl = mUrl;
}
/**
*
* @return The exponential moving average object instance
*/
public ExponentialMovingAverage getLatencyAverage(){
return latency;
}
/**
*
* @return The latest latency average value
*/
public double getLatencyValue() {
return latency.getAverage();
}
/**
* Method that updates the latency average with a new value.
* @param latency Most recent latency sample to be added to the exponential average
*/
public void addLatencyValue(double latency) {
this.latency.updateValue(latency);
}
@Override
public int compareTo(Object o) {
FullNode node = (FullNode) o;
return (int) Math.ceil(latency.getAverage() - node.getLatencyValue());
}
}

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package cy.agorise.graphenej.stats;
/**
* Class used to compute the Exponential Moving Average of a sequence of values.
* For more details see <a href="https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average">here</a>.
*/
public class ExponentialMovingAverage {
public static final double DEFAULT_ALPHA = 0.5;
private double alpha;
private Double accumulatedValue;
/**
* Constructor, which takes only the alpha parameter as an argument.
*
* @param alpha The coefficient alpha represents the degree of weighting decrease, a constant
* smoothing factor between 0 and 1. A higher alpha discounts older observations faster.
*/
public ExponentialMovingAverage(double alpha) {
this.alpha = alpha;
}
/**
* Method that updates the average with a new sample
* @param value New value
* @return The updated average value
*/
public double updateValue(double value) {
if (accumulatedValue == null) {
accumulatedValue = value;
return value;
}
double newValue = accumulatedValue + alpha * (value - accumulatedValue);
accumulatedValue = newValue;
return newValue;
}
/**
*
* @return Returns the current average value
*/
public double getAverage(){
return accumulatedValue == null ? 0 : accumulatedValue;
}
public void setAlpha(double alpha){
this.alpha = alpha;
this.accumulatedValue = null;
}
}

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package cy.agorise.graphenej.network;
import junit.framework.Assert;
import org.junit.Test;
public class FullNodeTest {
@Test
public void testFullNodeComparable(){
FullNode nodeA = new FullNode("wss://dummy");
FullNode nodeB = new FullNode("wss://dummy");
FullNode nodeC = new FullNode("wss://dummy");
nodeA.addLatencyValue(100);
nodeB.addLatencyValue(200);
nodeC.addLatencyValue(100);
Assert.assertTrue("Makes sure the node nodeA.compareTo(nodeB) returns a negative value", nodeA.compareTo(nodeB) < 0);
Assert.assertTrue("Makes sure nodeA.compareTo(nodeB) returns zero", nodeA.compareTo(nodeC) == 0);
Assert.assertTrue("Makes sure nodeB.compareTo(nodeA) returns a positive value", nodeB.compareTo(nodeA) > 0);
}
@Test
public void testFullNodeAverageLatency(){
FullNode fullNode = new FullNode("wss://dummy");
fullNode.getLatencyAverage().setAlpha(0.5);
fullNode.addLatencyValue(100);
Assert.assertEquals(100.0, fullNode.getLatencyValue());
fullNode.addLatencyValue(50);
Assert.assertEquals(75.0, fullNode.getLatencyValue());
}
}