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55 Implementation The LPCC receives the current sensor values with each simulation time update cycle (details of the scheduling can be found in 5.<strong>3.1</strong>) but the main processing of the LPCC is only called during specified time intervals as set in the configuration file of the simulator. When the main processing of the LPCC is called using its processSensorValues() method then the LPCC will create a set of indicator values for the sensor values cumulated. Using the State Cluster Space it will search for a similar indicator set that promises better performance and it will set the actuator according to the indicator set found. Finally the current indicator set will be added to the State Cluster Space. The LPCC is also used to check whether the current number of uncommitted Transaction moves exceeds the current actuator limit. Within the scheduling cycle the LP will call the isUncommittedMovesValueWithinActuatorRange() method of its LPControlComponent instance to perform this check. As a result the number of uncommitted Transaction moves passed in is compared to the maximum actuator limit determined by the mean actuator value and the standard deviation with a confidence level of 95% as described in [8]. The method will return false if the number of uncommitted Transaction moves exceeds the maximum actuator limit forcing the LP into cancelback mode (see 5.3.4). State Cluster Space The StateClusterSpace class encapsulates the functionality to store sets of indicator values and to return a similar indicator set for a given one. Each stored indicator set is treated as a vector in an n-dimensional vector space with n being the number of indicators per set. The similarity between two indicator sets is determined by their Euclidean vector distance. A clustering technique is used that groups similar indicator sets into clusters to limit the amount of memory required when large numbers of indicator sets are stored. The two main public methods provided by the StateClusterSpace class are addIndicatorSet() and getClosestIndicatorSetForHigherCommittedMoveRate(). The first method adds a new indicator set to the State Cluster Space and the second returns the indicator set most similar to the one passed in that has a higher CommittedMoveRate indicator value. Note that the two indicators AvgUncommittedMoves and CommittedMoveRate are ignored when determining the similarity by calculating the
Implementation Euclidean distance because AvgUncommittedMoves is directly linked to the actuator and CommittedMoveRate is the performance indicator that is hoped to be maximized. Test and Debugging of the Shock Resistant Time Warp and the State Cluster Space The State Cluster Space was tested and debugged using the test application class TestStateClusterSpaceApp, which allows for the StateClusterSpace class to be tested outside the parallel simulator. Using this class the detailed functionality of the State Cluster Space was tested using specific scenarios that would have been difficult to create within the parallel simulator. The test application class is left in the project so that possible future changes or enhancements to the StateClusterSpace class can also be tested outside the parallel simulator. The implementation of the Shock Resistant Time Warp algorithm was tested and debugged in the final version of the parallel simulator using a selection of different models of which a significant one was chosen for validation 5 in section 6.5. 5.3 Specific Implementation Details The following sections describe some specific implementation details of the parallel simulator. 5.<strong>3.1</strong> Scheduling The scheduling of the parallel simulator was implemented in two phases. The first part is the basic scheduling of the GPSS simulation that was implemented using the SimulationEngine class as described in section 5.2.2. This scheduling algorithm was later extended for the parallel simulation by the LogicalProcess class and the ParallelSimulationEngine class, which inherits from the SimulationEngine class as part of the Time Warp parallel simulator implementation phase described in 5.2.3. Basic GPSS Scheduling The basic GPSS scheduling is implemented using the functionality provided by the SimulationEngine class. A flowchart diagram of the scheduling algorithm is shown in Figure 17. As seen from this diagram the scheduling algorithm will first initialise the GENERATE blocks in order to create the first Transactions. Subsequent Transactions 56
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TRANSACTION-ORIENTED SIMULATION IN
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Table of Content II Table of Conten
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IV Table of Content 6 VALIDATION OF
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Figures VI Figures Figure 1: Ad Hoc
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Tables VIII Tables Table 1: Change
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2 Introduction synchronisation algo
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Reserved word: SEIZE Syntax: [] SEI
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Setting: LpccClusterNumber Default
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Appendix C: Simulator log4j loggers
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Logger: parallelJavaGpssSimulator.l
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114 Appendix D: Structure of the at
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!/+/.7/8$ Appendix E: Documentation
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Returns: Appendix E: Documentation
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Constructor Detail !"#$%&'$(#$)*$%+
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Validation 1.1, output of simulate:
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Validation 2, output of LP: Appendi
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Appendix F: Validation output logs
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Validation 2, output of simulate: A
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Validation 3.1, output of LP1: Appe
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simulation for time 2000) (628) 20:
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Validation 4, output of simulate: A
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Validation 6.2, output of LP2: (1)
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(32) (1,2) 0 765 Block: TRANSFER 0.
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