/**
* Retrieves the value associated with the provided iteration of the given optimizing job.
*
* @param iteration The job iteration for which to retrieve the value.
* @return The value associated with the provided iteration of the given optimizing job.
* @throws SLAMDException If a problem occurs while trying to determine the value for the given
* optimizing job iteration.
*/
@Override()
public double getIterationOptimizationValue(Job iteration) throws SLAMDException {
StatTracker[] trackers = iteration.getStatTrackers(optimizeStat);
if ((trackers == null) || (trackers.length == 0)) {
throw new SLAMDException(
"The provided optimizing job iteration did "
+ "not include any values for the statistic to "
+ "optimize, \""
+ optimizeStat
+ "\".");
}
StatTracker tracker = trackers[0].newInstance();
tracker.aggregate(trackers);
double summaryValue = tracker.getSummaryValue();
iteration.slamdServer.logMessage(
Constants.LOG_LEVEL_JOB_DEBUG,
"SingleStatisticWithReplicationLatency"
+ "OptimizationAlgorithm."
+ "getIterationOptimizationValue("
+ iteration.getJobID()
+ ") returning "
+ summaryValue);
return summaryValue;
}
/**
* Initializes this optimization algorithm with the provided set of parameters for the given
* optimizing job.
*
* @param optimizingJob The optimizing job with which this optimization algorithm will be used.
* @param parameters The parameter list containing the parameter values provided by the end user
* when scheduling the optimizing job.
* @throws InvalidValueException If the contents of the provided parameter list are not valid for
* use with this optimization algorithm.
*/
@Override()
public void initializeOptimizationAlgorithm(OptimizingJob optimizingJob, ParameterList parameters)
throws InvalidValueException {
this.optimizingJob = optimizingJob;
String[] monitorClients = optimizingJob.getResourceMonitorClients();
if ((monitorClients == null) || (monitorClients.length == 0)) {
throw new InvalidValueException(
"No resource monitor clients have been "
+ "requested for this optimizing job. "
+ "At least one is required to provide "
+ "replication latency data.");
}
// Get the optimization statistic parameter and name.
optimizeStatParameter = parameters.getMultiChoiceParameter(PARAM_OPTIMIZE_STAT);
if ((optimizeStatParameter == null) || (!optimizeStatParameter.hasValue())) {
throw new InvalidValueException("No value provided for the statistic " + "to optimize");
}
optimizeStat = optimizeStatParameter.getStringValue();
// Get the optimization type parameter and value.
optimizeTypeParameter = parameters.getMultiChoiceParameter(PARAM_OPTIMIZE_TYPE);
if ((optimizeTypeParameter == null) || (!optimizeTypeParameter.hasValue())) {
throw new InvalidValueException("No value provided for the " + "optimization type");
}
String optimizeTypeStr = optimizeTypeParameter.getStringValue();
if (optimizeTypeStr.equalsIgnoreCase(Constants.OPTIMIZE_TYPE_MAXIMIZE)) {
optimizeType = OPTIMIZE_TYPE_MAXIMIZE;
} else if (optimizeTypeStr.equalsIgnoreCase(Constants.OPTIMIZE_TYPE_MINIMIZE)) {
optimizeType = OPTIMIZE_TYPE_MINIMIZE;
} else {
throw new InvalidValueException(
"Invalid value \"" + optimizeTypeStr + "\" for optimization type.");
}
// Get the maximum percent increase parameter and value.
maxIncreaseParameter = parameters.getFloatParameter(PARAM_MAX_PERCENT_INCREASE);
if ((maxIncreaseParameter == null) || (!maxIncreaseParameter.hasValue())) {
throw new InvalidValueException(
"No value provided for the maximum "
+ "allowed percentage increase in "
+ "replication latency.");
}
maxIncrease = maxIncreaseParameter.getFloatValue();
// Get the maximum latency parameter and value.
maxLatencyParameter = parameters.getFloatParameter(PARAM_MAX_REPLICA_LATENCY);
if ((maxLatencyParameter == null) || (!maxLatencyParameter.hasValue())) {
maxLatency = -1.0;
} else {
maxLatency = maxLatencyParameter.getFloatValue();
}
// Get the minimum percent improvement required for a new best iteration.
minPctImprovement = 0.0F;
minPctImprovementParameter = parameters.getFloatParameter(PARAM_MIN_PCT_IMPROVEMENT);
if ((minPctImprovementParameter != null) && minPctImprovementParameter.hasValue()) {
minPctImprovement = minPctImprovementParameter.getFloatValue();
}
// See If the provided optimizing job has run any iterations so far. If so,
// then look through them to determine the best value so far.
bestValueSoFar = Double.NaN;
Job[] iterations = optimizingJob.getAssociatedJobs();
if (iterations != null) {
for (int i = 0; i < iterations.length; i++) {
try {
if (!isAcceptableReplicationLatency(iterations[i])) {
continue;
}
} catch (Exception e) {
}
StatTracker[] trackers = iterations[i].getStatTrackers(optimizeStat);
if ((trackers != null) && (trackers.length > 0)) {
StatTracker tracker = trackers[0].newInstance();
tracker.aggregate(trackers);
double value = tracker.getSummaryValue();
if (Double.isNaN(bestValueSoFar)) {
bestValueSoFar = value;
} else if ((optimizeType == OPTIMIZE_TYPE_MAXIMIZE)
&& (value > bestValueSoFar)
&& (value >= (bestValueSoFar + bestValueSoFar * minPctImprovement))) {
bestValueSoFar = value;
} else if ((optimizeType == OPTIMIZE_TYPE_MINIMIZE)
&& (value < bestValueSoFar)
&& (value <= (bestValueSoFar - bestValueSoFar * minPctImprovement))) {
bestValueSoFar = value;
}
}
}
}
SLAMDServer slamdServer = optimizingJob.slamdServer;
slamdServer.logMessage(
Constants.LOG_LEVEL_JOB_DEBUG,
"SingleStatisticWithReplicationLatencyOptimization"
+ "Algorithm.initializeOptimizationAlgorith("
+ optimizingJob.getOptimizingJobID()
+ ") best so far is "
+ String.valueOf(bestValueSoFar));
}
