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31 Parallel Transaction-oriented Simulation sensors the arithmetic mean value over the same time period. Table 4 shows the corresponding indicators. Sensor Type Description CommittedEvents cumulative total number of events committed SimulatedEvents cumulative total number of events simulated MsgsSent cumulative total number of (positive) messages sent AntiMsgsSent cumulative total number of anti-messages sent MsgsRcvd cumulative total number of (positive) messages received AntiMsgsRcvd cumulative total number of anti-messages received EventsRollback cumulative total number of events rolled back EventsUsed point sample momentary number of events in use Table 3: Shock Resistant Time Warp sensors Indicator Description EventRate number of events committed per second SimulationRate number of events simulated per second MsgsSentRate number of (positive) messages sent per second AntiMsgsSentRate number of anti-messages sent per second MsgsRcvdRate number of (positive) messages received per second AntiMsgsRcvd number of anti-messages received per second EventsRollbackRate number of events rolled back per second MemoryConsumption average number of events in use Table 4: Shock Resistant Time Warp indicators Two of these indicators are slightly special. The EventRate indicator, which describes the number of events committed per second during a time period, is the performance indicator used to identify how much useful work has been performed. And the actuator value MemoryLimit is derived from the MemoryConsumption indicator. For a state vector with n different indicator values the LPCC will use an n-dimensional state vector
32 Parallel Transaction-oriented Simulation space to store and compare the state vectors. The similarity of two state vectors within this state vector space is characterised by the Euclidean distance between the vectors. When searching for the most similar historic state vector that has a higher performance indicator then the Euclidean distance is calculated by ignoring the indicators EventRate and MemoryConsumption because the EventRate is the indicator that the LPCC is trying to optimise and the MemoryConsumption is directly linked to the MemoryLimit actuator controlled by the LPCC. Keeping a full history of the past state vectors would require a large amount of memory and would create an exponentially increasing performance overhead. For these reasons the Shock Resistant Time Warp algorithm uses a clustering mechanism to cluster similar state vectors. The algorithm will keep a defined number of clusters. At first each new state is stores as a new cluster but when the cluster limit is reached then new states are added to existing clusters if the distance between the state and the cluster is smaller than any of the inter-cluster distances and otherwise the two closest clusters are merged into one and the second cluster is replaced with the new state vector. The clustering mechanism limits the total number of clusters stored and at the same time clusters will move their location within the state space to reflect the mean position of the state vectors they represent. The Shock Resistant Time Warp algorithm as described in [8] is specific to discrete event simulation but it can also be applied to transaction-oriented simulation because of the equivalence between events in discrete event simulation and the movement of Transactions in transaction-oriented simulation as outlined in 4.1.1. Because the transaction-oriented simulation does not know events as such the names of the sensors and indicators described above need to be changed to avoid confusion when applying the Shock Resistant Time Warp algorithm to transaction-oriented simulation. The two tables below show the sensor and indicator names that will be used for this work.
Page 1 and 2: TRANSACTION-ORIENTED SIMULATION IN
Page 3 and 4: Table of Content II Table of Conten
Page 5 and 6: IV Table of Content 6 VALIDATION OF
Page 7 and 8: Figures VI Figures Figure 1: Ad Hoc
Page 9 and 10: Tables VIII Tables Table 1: Change
Page 11 and 12: 2 Introduction synchronisation algo
Page 13 and 14: 2 Fundamental Concepts Fundamental
Page 15 and 16: Figure 1: Ad Hoc Grid architecture
Page 17 and 18: 8 Fundamental Concepts In continuou
Page 19 and 20: 10 Fundamental Concepts Two other a
Page 21 and 22: 12 Fundamental Concepts known as st
Page 23 and 24: 14 Fundamental Concepts event execu
Page 25 and 26: 3 Ad Hoc Grid Aspects 16 Ad Hoc Gri
Page 27 and 28: 18 Ad Hoc Grid Aspects resources an
Page 29 and 30: 20 Ad Hoc Grid Aspects means that a
Page 31 and 32: Parallel Transaction-oriented Simul
Page 33 and 34: 24 Parallel Transaction-oriented Si
Page 35 and 36: 4.2 Synchronisation algorithm 26 Pa
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Page 49 and 50: 5 Implementation 40 Implementation
Page 51 and 52: 42 Implementation confirm a provisi
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Page 55 and 56: 46 Implementation A detailed descri
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Page 59 and 60: 50 Implementation GPSS test models
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Page 75 and 76: 66 Implementation provisional simul
Page 77 and 78: Implementation relatively basic per
Page 79 and 80: 70 Implementation log4j.logger.para
Page 81 and 82: proactive-log4j 72 Implementation T
Page 83 and 84: 74 Implementation Memory Allocation
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82 Validation of the Parallel Simul
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6.4 Validation 4 84 Validation of t
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… LpccEnabled=true LpccClusterNum
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Validation 5.2 Validation of the Pa
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7 Conclusions 96 Conclusions Even s
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References 98 References [1] Amin K
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[19] Krafft G. Parallelisation of T
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Appendix A: Detailed GPSS Syntax 10
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Reserved word: DEPART Syntax: [] DE
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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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Appendix E: Documentation of select
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!/+/.7/8$ Appendix E: Documentation
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Returns: Appendix E: Documentation
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!"#$%&'()#%*+,"-*.# Appendix E: Doc
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Constructor Detail !"#$%&'$(#$)*$%+
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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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Validation 4, output of LP1: Append
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simulation for time 2000) (628) 20:
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Validation 4, output of simulate: A
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(135) 19:37:56,477 Total transactio
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(68) 19:37:56,619 by the transactio
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(1295) 14:25:22,740 Simulation stop
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(68) 14:25:22,888 by the transactio
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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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