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2.5.1 Conservative Algorithms 11 Fundamental Concepts Conservative algorithms prevent that causal dependent events are executed out of order by executing only “safe” events [12]. An LP will consider an event to be “safe” if it is guaranteed that the LP cannot later receive an event with an earlier time stamp. The main task of conservative algorithms is to provide such guarantees so that LPs can determine which of the events are guaranteed and can be executed. Definition: guaranteed event An event e with the timestamp t which is to be executed in LPi is called guaranteed event if LPi knows all events with a timestamp t’ < t that it will need to execute during the whole simulation. One drawback of conservative algorithms is that LPs will have to wait or block if they don’t have any “safe” events. This can even lead to deadlocks where all LPs are waiting for guarantees so that they can execute their events. Many of the conservative algorithms also require additional information about the simulation model like the communication topology 1 or lookahead 2 information. Further details about conservative algorithms can be found in [12], [34]. 2.5.2 Optimistic Algorithms Optimistic algorithms allow causal dependent events to be executed out of order first but they provide mechanisms to later recover from possible violations of the causal order. The best-known and most analysed optimistic algorithm is Time Warp [16] on which many other optimistic algorithms are based. In Time Warp an LP will first execute all events in its local event list but if it receives an event from another LP with a time stamp smaller than the ones already executed then it will rollback all its events that should have been executed after the event just received. State checkpointing 3 (also 1 describes which LP can send events to which other LP 2 is the models ability to predict the future course of events [5] 3 the state of the simulation is saved into a state list together with the current simulation time after the execution of each event or in other defined intervals
12 Fundamental Concepts known as state saving) is used in order to be able to rollback the state of the LP if required. LP1 e1 5 e2 10 rollback e3 8 from LPn e3 8 e2 10 Figure 5: Event execution with rollback [21] simulated time Figure 5 shows an example LP that performs the local events e1 and e2 with the time stamps of 5 and 10 but then receives another event e3 with a time stamp of 8 from a different LP. At this point LP1 will rollback the execution of event e2 then execute the newly received event e3 and afterwards execute the event e2 again in order to retain the causal order of the events. The rollback of already executed events can result in having to rollback events that have already been sent to other LPs. To archive this anti-events are sent to the same LPs like the original events, which will result in the original event being deleted if it has not been executed yet or in a rollback of the received event and all later events. These rollbacks and anti-events can lead to large cascaded rollbacks and many events having to be executed again. It is also possible that after the rollback the same events that have been rolled back are executed again sending out the same events to other LPs for which anti-events were sent during the rollback. In order to avoid this a different mechanism for cancelling events exists which is called lazy cancellation [13]. Compared to the original cancellation mechanism that is also called aggressive cancellation and was suggested by Jefferson [16], the lazy cancellation mechanism does not send out anti-events immediately during the rollback but instead keeps a history of the events sent that have been rolled back and only sends out anti-events when the event that was sent and rolled back is not re-executed. If for instance the LP is rolled back from the simulation time t’ to the new simulation time t’’ t’ then the lazy cancellation mechanism will re-execute the events in the interval [t’’,t’] and will only sent anti- events for events that had been sent during the first execution of that time interval but that were not generated during the re-execution. The difference between aggressive
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
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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
Page 49 and 50: 5 Implementation 40 Implementation
Page 51 and 52: 42 Implementation confirm a provisi
Page 53 and 54: 44 Implementation large numerical p
Page 55 and 56: 46 Implementation A detailed descri
Page 57 and 58: 48 Implementation But in a parallel
Page 59 and 60: 50 Implementation GPSS test models
Page 61 and 62: Simulation Controller side 52 Imple
Page 63 and 64: 54 Implementation technique describ
Page 65 and 66: Implementation Euclidean distance b
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Page 69 and 70: 60 Implementation The algorithm wil
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No Start Is Active Object body acti
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5.3.2 GVT Calculation and End of Si
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66 Implementation provisional simul
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Implementation relatively basic per
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70 Implementation log4j.logger.para
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proactive-log4j 72 Implementation T
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74 Implementation Memory Allocation
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76 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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!"#"$$%$&"'"(!))*+,-$"./#0$! 1$"))
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!/+/.7/8$ Appendix E: Documentation
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Returns: Appendix E: Documentation
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!"#$%&''$()*)")$ +',)$-)$. #,/. !"#
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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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