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1 Introduction 1 Introduction Computer Simulation is one of the oldest areas in Computer Science. It provides answers about the behaviour of real or imaginary systems that otherwise could only be gained under great expenditure of time, with high costs or that could not be gained at all. Computer Simulation uses simulation models that are usually simpler than the systems they represent but that are expected to behave as analogue as possible or as required. The growing demand of complex Computer Simulations for instance in engineering, military, biology and climate research has also lead to a growing demand in computing power. One possibility to reduce the runtime of large, complex Computer Simulations is to perform such simulations distributed on several CPUs or computing nodes. This has induced the availability of high-performance parallel computer systems. Even so the performance of such systems has constantly increased, the ever-growing demand to simulate more and more complex systems means that suitable high- performance systems are still very expensive. Grid computing promises to provide large-scale computing resources at lower costs by allowing several organisations to share their resources. But traditional Computing Grids are relatively static environments that require a dedicated administrative authority and are therefore less well suited for transient short-term collaborations and small organisations with fewer resources. Ad Hoc Grids provide such a dynamic and transient resource-sharing infrastructure that allows even small organisations or individual users to form Computing Grids. They will make Grid computing and Grid resources widely available to small organisations and mainstream users allowing them to perform resource intensive computing tasks like Computer Simulations. There are several approaches to performing Computer Simulations distributed across a parallel computer system. The space-parallel approach [12] is one of these approaches that is robust, applicable to many different simulation types and that can be used to speed up single simulation runs. It requires the simulation model to be partitioned into relatively independent sub-systems that are then performed in parallel on several nodes. Synchronisation between these nodes is still required because the model sub-systems are not usually fully independent. A lot of past research has concentrated on different
2 Introduction synchronisation algorithms for parallel simulation. Some of these are only suitable for certain types of parallel systems, like for instance shared memory systems. This work investigates the possibility of performing parallel transaction-oriented simulation in an Ad Hoc Grid environment with the main focus on the aspects of parallel simulation. Potential synchronisation algorithms and other simulation aspects are analysed in respect of their suitability for transaction-oriented simulation and Ad Hoc Grids as the target environment and the chosen solutions are described and reasons for their choice given. A past attempt to investigate the parallelisation of transaction- oriented simulation was presented in [19] with the result that the synchronisation algorithm employed was not well suited for transaction-oriented simulation. Lessons from this past attempt have been learned and included in the considerations of this work. Furthermore this work outlines certain requirements that a Grid environment needs to fulfil in order to be appropriate for Ad Hoc Grids. The proposed solutions are demonstrated by implementing a Java-based parallel transaction-oriented simulator using the Grid middleware ProActive [15], which fulfils the requirements described before. The specific simulation type transaction-oriented simulation was chosen because it is still taught at many universities and is therefore well known. It uses a relatively simple language for the modelling that does not require extensive programming skills and it is a special type of discrete event simulation so that most findings can also be applied to this wider simulation classification. The remainder of this report is organised as follows. Section 2 introduces the fundamental concepts and terminology essential for the understanding of this work. In section 3 the specific requirements of Ad Hoc Grids are outlined and the Grid middleware ProActive is briefly described as an environment that fulfils these requirements. Section 4 focuses on the aspects of parallel simulation and their application to transaction-oriented simulation. Past research results are discussed, requirements for a suitable synchronisation algorithm outlined and the most promising algorithm selected. This section also addresses other points related to parallel transaction-oriented simulation like GVT calculation, handling of the simulation end, suitable cancellation techniques and the influence of the model partitioning. Section 5, which is the largest section of this report, describes the implementation of the parallel
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: Tables VIII Tables Table 1: Change
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
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Simulation Controller side 52 Imple
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54 Implementation technique describ
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Implementation Euclidean distance b
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58 Implementation chain at the corr
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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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