package br.ufrgs.inf.prosoft.tigris.sampling;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import org.apache.commons.math3.util.FastMath;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
public class PerformanceBaselineDataSet {
Logger logger = LoggerFactory.getLogger(PerformanceBaselineDataSet.class);
double weights[] = {0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 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0.12, 0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.41, 0.41, 0.41, 0.41, 0.41, 0.41, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42, 0.43, 0.43, 0.43, 0.43, 0.43, 0.43, 0.44, 0.44, 0.44, 0.44, 0.44, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.46, 0.46, 0.46, 0.46, 0.46, 0.46, 0.47, 0.47, 0.47, 0.47, 0.47, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.49, 0.49, 0.49, 0.49, 0.49, 0.5, 0.5, 0.5, 0.5, 0.5, 0.51, 0.51, 0.51, 0.51, 0.51, 0.52, 0.52, 0.52, 0.52, 0.52, 0.53, 0.53, 0.53, 0.53, 0.53, 0.54, 0.54, 0.54, 0.54, 0.54, 0.55, 0.55, 0.55, 0.55, 0.56, 0.56, 0.56, 0.56, 0.56, 0.57, 0.57, 0.57, 0.57, 0.57, 0.58, 0.58, 0.58, 0.58, 0.59, 0.59, 0.59, 0.59, 0.6, 0.6, 0.6, 0.6, 0.6, 0.61, 0.61, 0.61, 0.61, 0.62, 0.62, 0.62, 0.62, 0.63, 0.63, 0.63, 0.63, 0.64, 0.64, 0.64, 0.64, 0.65, 0.65, 0.65, 0.65, 0.66, 0.66, 0.66, 0.66, 0.67, 0.67, 0.67, 0.67, 0.68, 0.68, 0.68, 0.68, 0.69, 0.69, 0.69, 0.69, 0.7, 0.7, 0.7, 0.71, 0.71, 0.71, 0.71, 0.72, 0.72, 0.72, 0.73, 0.73, 0.73, 0.73, 0.74, 0.74, 0.74, 0.74, 0.75, 0.75, 0.75, 0.76, 0.76, 0.76, 0.77, 0.77, 0.77, 0.77, 0.78, 0.78, 0.78, 0.79, 0.79, 0.79, 0.8, 0.8, 0.8, 0.8, 0.81, 0.81, 0.81, 0.82, 0.82, 0.82, 0.83, 0.83, 0.83, 0.84, 0.84, 0.84, 0.85, 0.85, 0.85, 0.86, 0.86, 0.86, 0.87, 0.87, 0.87, 0.88, 0.88, 0.88, 0.89, 0.89, 0.89, 0.9, 0.9, 0.9, 0.91, 0.91, 0.91, 0.92, 0.92, 0.92, 0.93, 0.93, 0.94, 0.94, 0.94, 0.95, 0.95, 0.95, 0.96, 0.96, 0.96, 0.97, 0.97, 0.98, 0.98, 0.98, 0.99, 0.99};
private Map<Granularity, DescriptiveStatistics> granularityBaseline = new ConcurrentHashMap<>();
private DescriptiveStatistics overallBaseline = new DescriptiveStatistics(1200);
private int n = 0;
public void addItem(Granularity item, long executionTime) {
DescriptiveStatistics statistics = granularityBaseline.getOrDefault(item, new DescriptiveStatistics(1200));
statistics.addValue(executionTime);
granularityBaseline.put(item, statistics);
n++;
overallBaseline.addValue(executionTime);
}
//TODO compare baseline againts baseline, not against sampled items
//Solução 1: Guarda o baseline com a média mais baixa de todas - em teoria esse é o mais próximo da aplicação "real", sem influencia de monitoração ou falta de recursos - mínimo global
//compara o sample com o minimo global
//need to detect global max and min to avoid "getting used" to bad things
//diminuir as rodadas de baseline (espaços maiores), guardar max and min, olhar pra quartis talvez?
//se o q1 ta ficando maior, significa que a carga e overhead estão subindo, se está menor, está caindo
//questões: a aplicação está ok sem monitoração? essa é a melhor performance dela? é a pior? o quao distante dos topos está
//não comparar baseline com sample, não faz sentido - ou é sample com sample, ou é baseline com sample
//baseline com sample, diz o overhead do sampling
//baseline com baseline diz a carga da aplicação
public Apdex getApdexResultsPerEvent(Map<Granularity, DescriptiveStatistics> sampledDataSet) {
long satisfied = 0, tolerated = 0, n = 0;
for (Map.Entry<Granularity, DescriptiveStatistics> baselineEntry : granularityBaseline.entrySet()) {
DescriptiveStatistics stats = baselineEntry.getValue();
double mean = stats.getMean();
double std = stats.getStandardDeviation();
if (stats.getN() == stats.getWindowSize()) {
mean = new Mean().evaluate(stats.getValues(), weights);
std = FastMath.sqrt(new Variance().evaluate(stats.getValues(), weights));
}
double meanPlusStd = mean + std;
DescriptiveStatistics descriptiveStatistics = sampledDataSet.get(baselineEntry.getKey());
if (descriptiveStatistics == null)
continue;
for (double value : descriptiveStatistics.getValues()) {
// if (value <= mean) {
// satisfied++;
// }
// if (value > mean &&
// value < meanPlusStd) {
// tolerated++;
// }
if (value <= meanPlusStd) {
satisfied++;
}
if (value > meanPlusStd &&
value < mean + (2 * std)) {
tolerated++;
}
n++;
}
}
return new Apdex(satisfied, tolerated, n);
}
/**
* Compare the results against the overall statistics
* However, some methods may be really fast and some really huge -
* if any discrepancy found, maybe we should use getApdexResultsPerEvent
*
* @param sampledDataSet
* @param lastSampledTimes
* @return
*/
public Apdex getApdexResults(Map<Granularity, DescriptiveStatistics> sampledDataSet, DescriptiveStatistics lastSampledTimes) {
long satisfied = 0, tolerated = 0, n = 0;
double overallMean = getOverallAvg();
double overallStd = getOverallStd();
if (overallBaseline.getN() == overallBaseline.getWindowSize()) {
overallMean = new Mean().evaluate(overallBaseline.getValues(), weights);
overallStd = FastMath.sqrt(new Variance().evaluate(overallBaseline.getValues(), weights));
}
double meanPlusStd = overallMean + overallStd;
for (DescriptiveStatistics granularityTraces : sampledDataSet.values()) {
for (double value : granularityTraces.getValues()) {
// for (double value : lastSampledTimes.getValues()) {
// if (value <= overallMean) {
// satisfied++;
// }
// if (value > overallMean &&
// value < meanPlusStd) {
// tolerated++;
// }
if (value <= meanPlusStd) {
satisfied++;
}
if (value > meanPlusStd &&
value < overallMean + (2 * overallStd)) {
tolerated++;
}
n++;
}
}
return new Apdex(satisfied, tolerated, n);
}
public double getOverallAvg() {
return overallBaseline.getMean();
}
public double getOverallStd() {
return overallBaseline.getStandardDeviation();
}
public long getTotalItems() {
return n;
}
public void clear() {
//TODO should we no clean this?
n = 0;
// overallBaseline.clear();
// granularityBaseline.clear();
}
}