/**
* Indicates whether this optimization algorithm may be used when running the specified type of
* job. This algorithm is only available for jobs that report at least one "searchable" stat
* tracker.
*
* @param jobClass The job class for which to make the determination.
* @return <CODE>true</CODE> if this optimization algorithm may be used with the provided job
* class, or <CODE>false</CODE> if not.
*/
@Override()
public boolean availableWithJobClass(JobClass jobClass) {
StatTracker[] jobStats = jobClass.getStatTrackerStubs("", "", 1);
if ((jobStats == null) || (jobStats.length == 0)) {
return false;
}
for (int i = 0; i < jobStats.length; i++) {
if (jobStats[i].isSearchable()) {
return true;
}
}
return false;
}
/**
* Retrieves a set of parameter stubs that should be used to prompt the end user for the settings
* to use when executing the optimizing job.
*
* @param jobClass The job class that will be used for the optimizing job.
* @return A set of parameter stubs that should be used to prompt the end user for the settings to
* use when executing the optimizing job.
*/
@Override()
public ParameterList getOptimizationAlgorithmParameterStubs(JobClass jobClass) {
// First, compile a list of all the "searchable" statistics that this job
// reports it collects.
ArrayList<String> availableStatList = new ArrayList<String>();
StatTracker[] jobStats = jobClass.getStatTrackerStubs("", "", 1);
for (int i = 0; i < jobStats.length; i++) {
if (jobStats[i].isSearchable()) {
availableStatList.add(jobStats[i].getDisplayName());
}
}
int numAvailable = availableStatList.size();
if (numAvailable == 0) {
return new ParameterList();
}
String[] searchableStatNames = new String[numAvailable];
availableStatList.toArray(searchableStatNames);
if (optimizeStat == null) {
optimizeStat = searchableStatNames[0];
}
String[] optimizationTypes = {
Constants.OPTIMIZE_TYPE_MAXIMIZE, Constants.OPTIMIZE_TYPE_MINIMIZE
};
String optimizeTypeStr;
switch (optimizeType) {
case OPTIMIZE_TYPE_MAXIMIZE:
optimizeTypeStr = Constants.OPTIMIZE_TYPE_MAXIMIZE;
break;
case OPTIMIZE_TYPE_MINIMIZE:
optimizeTypeStr = Constants.OPTIMIZE_TYPE_MINIMIZE;
break;
default:
optimizeTypeStr = Constants.OPTIMIZE_TYPE_MAXIMIZE;
break;
}
optimizeStatParameter =
new MultiChoiceParameter(
PARAM_OPTIMIZE_STAT,
"Statistic to Optimize",
"The name of the statistic for which to " + "try to find the optimal value.",
searchableStatNames,
optimizeStat);
optimizeTypeParameter =
new MultiChoiceParameter(
PARAM_OPTIMIZE_TYPE,
"Optimization Type",
"The type of optimization to perform for " + "the statistic to optimize.",
optimizationTypes,
optimizeTypeStr);
maxLatencyParameter =
new FloatParameter(
PARAM_MAX_REPLICA_LATENCY,
"Maximum Acceptable Replication Latency (ms)",
"The maximum average replication latency in "
+ "milliseconds that will be allowed for an "
+ "iteration to be considered acceptable. A "
+ "negative value indicates that there will be no "
+ "maximum latency.",
false,
(float) maxLatency);
maxIncreaseParameter =
new FloatParameter(
PARAM_MAX_PERCENT_INCREASE,
"Maximum Acceptable Percent Increase in Latency",
"The maximum percentage of increase in "
+ "replication latency that will be allowed for an "
+ "iteration to be considered acceptable.",
true,
(float) maxIncrease,
true,
(float) 0.0,
false,
(float) 0.0);
minPctImprovementParameter =
new FloatParameter(
PARAM_MIN_PCT_IMPROVEMENT,
"Min. % Improvement for New Best Iteration",
"The minimum percentage improvement in "
+ "performance that an iteration must have over "
+ "the previous best to be considered the new best "
+ "iteration.",
false,
minPctImprovement,
true,
0.0F,
false,
0.0F);
Parameter[] algorithmParams = {
new PlaceholderParameter(),
optimizeStatParameter,
optimizeTypeParameter,
maxLatencyParameter,
maxIncreaseParameter,
minPctImprovementParameter
};
return new ParameterList(algorithmParams);
}
/**
* Retrieves a set of parameter stubs that should be used to prompt the end user for the settings
* to use when executing the optimizing job.
*
* @param jobClass The job class that will be used for the optimizing job.
* @return A set of parameter stubs that should be used to prompt the end user for the settings to
* use when executing the optimizing job.
*/
@Override()
public ParameterList getOptimizationAlgorithmParameterStubs(JobClass jobClass) {
// First, compile a list of all the "searchable" statistics that this job
// reports it collects.
ArrayList<String> availableStatList = new ArrayList<String>();
StatTracker[] jobStats = jobClass.getStatTrackerStubs("", "", 1);
for (int i = 0; i < jobStats.length; i++) {
if (jobStats[i].isSearchable()) {
availableStatList.add(jobStats[i].getDisplayName());
}
}
int numAvailable = availableStatList.size();
if (numAvailable == 0) {
return new ParameterList();
}
String[] searchableStatNames = new String[numAvailable];
availableStatList.toArray(searchableStatNames);
if (optimizeStat == null) {
optimizeStat = searchableStatNames[0];
}
String[] optimizationTypes = {
Constants.OPTIMIZE_TYPE_MAXIMIZE, Constants.OPTIMIZE_TYPE_MINIMIZE
};
String optimizeTypeStr;
switch (optimizeType) {
case OPTIMIZE_TYPE_MAXIMIZE:
optimizeTypeStr = Constants.OPTIMIZE_TYPE_MAXIMIZE;
break;
case OPTIMIZE_TYPE_MINIMIZE:
optimizeTypeStr = Constants.OPTIMIZE_TYPE_MINIMIZE;
break;
default:
optimizeTypeStr = Constants.OPTIMIZE_TYPE_MAXIMIZE;
break;
}
String[] utilizationComponents = {
UTILIZATION_COMPONENT_USER_STRING,
UTILIZATION_COMPONENT_SYSTEM_STRING,
UTILIZATION_COMPONENT_BUSY_STRING
};
String componentStr;
switch (utilizationComponent) {
case UTILIZATION_COMPONENT_USER_TIME:
componentStr = UTILIZATION_COMPONENT_USER_STRING;
break;
case UTILIZATION_COMPONENT_SYSTEM_TIME:
componentStr = UTILIZATION_COMPONENT_SYSTEM_STRING;
break;
case UTILIZATION_COMPONENT_BUSY_TIME:
componentStr = UTILIZATION_COMPONENT_BUSY_STRING;
break;
default:
componentStr = UTILIZATION_COMPONENT_BUSY_STRING;
break;
}
optimizeStatParameter =
new MultiChoiceParameter(
PARAM_OPTIMIZE_STAT,
"Statistic to Optimize",
"The name of the statistic for which to " + "try to find the optimal value.",
searchableStatNames,
optimizeStat);
optimizeTypeParameter =
new MultiChoiceParameter(
PARAM_OPTIMIZE_TYPE,
"Optimization Type",
"The type of optimization to perform for " + "the statistic to optimize.",
optimizationTypes,
optimizeTypeStr);
maxUtilizationParameter =
new FloatParameter(
PARAM_MAX_CPU_UTILIZATION,
"Maximum Acceptable CPU Utilization",
"The maximum CPU utilization that will be "
+ "allowed for the specified CPU component for "
+ "an iteration to be considered acceptable.",
true,
(float) maxUtilization,
true,
(float) 0.0,
true,
(float) 100.0);
utilizationComponentParameter =
new MultiChoiceParameter(
PARAM_UTILIZATION_COMPONENT,
"Utilization Component to Consider",
"The CPU utilization component to consider "
+ "when deciding whether an iteration is "
+ "acceptable.",
utilizationComponents,
componentStr);
minPctImprovementParameter =
new FloatParameter(
PARAM_MIN_PCT_IMPROVEMENT,
"Min. % Improvement for New Best Iteration",
"The minimum percentage improvement in "
+ "performance that an iteration must have over "
+ "the previous best to be considered the new best "
+ "iteration.",
false,
minPctImprovement,
true,
0.0F,
false,
0.0F);
Parameter[] algorithmParams = {
new PlaceholderParameter(),
optimizeStatParameter,
optimizeTypeParameter,
maxUtilizationParameter,
utilizationComponentParameter,
minPctImprovementParameter
};
return new ParameterList(algorithmParams);
}