| 1 | package de.uka.ipd.sdq.pipesandfilters.framework.recorder; |
| 2 | |
| 3 | import java.util.LinkedList; |
| 4 | import java.util.Vector; |
| 5 | |
| 6 | import javax.measure.Measure; |
| 7 | import javax.measure.quantity.Quantity; |
| 8 | |
| 9 | import de.uka.ipd.sdq.pipesandfilters.framework.MetaDataInit; |
| 10 | import de.uka.ipd.sdq.pipesandfilters.framework.PipeData; |
| 11 | |
| 12 | /** |
| 13 | * This recorder calculates the sliding mean, i.e. the average value of a |
| 14 | * specified number of last result tuple element whenever a new pipe data |
| 15 | * element is received. |
| 16 | * |
| 17 | * @author Baum |
| 18 | * |
| 19 | */ |
| 20 | public class SlidingMeanRecorder extends AggregationRecorder { |
| 21 | private LinkedList<PipeData> dataQueue = new LinkedList<PipeData>(); |
| 22 | private int dataQueueSize = 0; |
| 23 | |
| 24 | /** |
| 25 | * The constructor of SlidingMeanRecorder. |
| 26 | * |
| 27 | * @param writeStrategy |
| 28 | * The write strategy of the recorder. |
| 29 | * @param dataQueueSize |
| 30 | * The window size of the sliding mean value, i.e. the number of |
| 31 | * last incoming result tuples the mean is computed on. |
| 32 | */ |
| 33 | public SlidingMeanRecorder(IAggregationWriteStrategy writeStrategy, |
| 34 | int dataQueueSize) { |
| 35 | super(writeStrategy); |
| 36 | this.dataQueueSize = dataQueueSize; |
| 37 | } |
| 38 | |
| 39 | /** |
| 40 | * This method initializes the aggregation write strategy, providing it with |
| 41 | * all information that is necessary. |
| 42 | * |
| 43 | * @param metaData |
| 44 | * The meta data for the incoming result tuples. |
| 45 | */ |
| 46 | protected void initialize(MetaDataInit metaData) { |
| 47 | |
| 48 | writeStrategy.initialize(metaData); |
| 49 | |
| 50 | int aggregatedMetricIndex = metaData.getMeasuredMetrics().size() - 1; |
| 51 | |
| 52 | // Create initializing meta data for the aggregation to initialize the |
| 53 | // aggregation write strategy. |
| 54 | AggregationMetaDataInit aggregationMetaData = new AggregationMetaDataInit( |
| 55 | aggregatedMetricIndex); |
| 56 | aggregationMetaData.setAggregationFunctionName("Sliding Mean"); |
| 57 | aggregationMetaData |
| 58 | .setAggregationFunctionDescription("Computes the average value of the last element of the result tuple at every processData step."); |
| 59 | aggregationMetaData.setValid(false); |
| 60 | |
| 61 | ((IAggregationWriteStrategy) writeStrategy) |
| 62 | .initializeAggregatedMeasurements(aggregationMetaData); |
| 63 | } |
| 64 | |
| 65 | /** |
| 66 | * This method calculates the sliding mean for each incoming data element |
| 67 | * with the specified window size. |
| 68 | * |
| 69 | * @param data |
| 70 | * The data to be processed. |
| 71 | */ |
| 72 | protected void processData(PipeData data) { |
| 73 | // add element to data queue |
| 74 | dataQueue.addLast(data); |
| 75 | if (dataQueue.size() > dataQueueSize) { |
| 76 | dataQueue.remove(); |
| 77 | } |
| 78 | |
| 79 | // Aggregation is always performed on the last result tuple element |
| 80 | Measure<?, ? extends Quantity> measure = data.getTupleElement(data |
| 81 | .getTupleSize() - 1); |
| 82 | |
| 83 | // Return value |
| 84 | Measure<?, ? extends Quantity> resultMeasure = Measure.valueOf(0, |
| 85 | measure.getUnit()); |
| 86 | |
| 87 | if (measure.getValue() instanceof Long) { |
| 88 | double value = 0; |
| 89 | for (PipeData p : dataQueue) { |
| 90 | value += (Long) p.getTupleElement(data.getTupleSize() - 1) |
| 91 | .getValue(); |
| 92 | } |
| 93 | resultMeasure = Measure.valueOf(value / dataQueue.size(), measure |
| 94 | .getUnit()); |
| 95 | } else if (measure.getValue() instanceof Integer) { |
| 96 | double value = 0; |
| 97 | for (PipeData p : dataQueue) { |
| 98 | value += (Integer) p.getTupleElement(data.getTupleSize() - 1) |
| 99 | .getValue(); |
| 100 | } |
| 101 | resultMeasure = Measure.valueOf(value / dataQueue.size(), measure |
| 102 | .getUnit()); |
| 103 | } else if (measure.getValue() instanceof Double) { |
| 104 | double value = 0; |
| 105 | for (PipeData p : dataQueue) { |
| 106 | value += (Double) p.getTupleElement(data.getTupleSize() - 1) |
| 107 | .getValue(); |
| 108 | } |
| 109 | resultMeasure = Measure.valueOf(value / dataQueue.size(), measure |
| 110 | .getUnit()); |
| 111 | } else if (measure.getValue() instanceof Float) { |
| 112 | double value = 0; |
| 113 | for (PipeData p : dataQueue) { |
| 114 | value += (Float) p.getTupleElement(data.getTupleSize() - 1) |
| 115 | .getValue(); |
| 116 | } |
| 117 | resultMeasure = Measure.valueOf(value / dataQueue.size(), measure |
| 118 | .getUnit()); |
| 119 | } |
| 120 | |
| 121 | Vector<Measure<?, ? extends Quantity>> aggregatedTuple = new Vector<Measure<?, ? extends Quantity>>(); |
| 122 | aggregatedTuple.add(resultMeasure); |
| 123 | PipeData aggregatedData = new PipeData(aggregatedTuple); |
| 124 | |
| 125 | writeStrategy.writeData(aggregatedData); |
| 126 | } |
| 127 | |
| 128 | /** |
| 129 | * This method tells the write Strategy's flush method. |
| 130 | */ |
| 131 | protected void flush() { |
| 132 | writeStrategy.flush(); |
| 133 | } |
| 134 | } |