3.1 MB - Evernote

More documents

Recommendations

Info

13 Fundamental Concepts cancellation and lazy cancellation can be seen in the following diagram. In this diagram the event index is describing the scheduled time of the event. LP1 LP2 LP3 e1 e3 ex Aggressive cancellation e2 e4 e2 e3 e4 e4- e4 e4 time LP1 LP2 LP3 e1 e3 e4 e2 Lazy cancellation e4 e2 e3 time ex- Event for other LP Rollback Anti-event for other LP Figure 6: Comparison of aggressive and lazy cancellation As shown in Figure 6 lazy cancellation can reduce cascaded rollbacks but it can also allow false events to propagate further and therefore lead to longer cascaded rollbacks when such false events are cancelled. The concept of Global Virtual Time (GVT) is used to regain memory and to control the overall global progress of the simulation. The GVT is defined as the minimum of the local simulation time, also called Local Virtual Time (LVT), of all LPs and of the time stamps of all events that have been send but not yet processed by the receiving LP [16]. The GVT describes the minimum simulation time any unexecuted event can have at a particular point in real time. It therefore acts as a guarantee for all executed events with a time stamp smaller than the GVT, which can now be deleted. Further memory is freed by removing all state checkpoints with a virtual time less than the GVT except the one closest to the GVT. Both conservative and optimistic algorithms have their advantages and disadvantages. The speedup of conservative algorithms can be limited because only guarantied events are executed. Compared to conservative algorithm optimistic algorithms can offer grater exploitation of parallelism [5] and they are less reliant on application specific information [11] or information about the communication topology. But optimistic algorithms have the overhead of maintaining rollback information and over-optimistic
14 Fundamental Concepts event execution in some LPs can lead to frequent and cascaded rollbacks and result in a degradation of the effective processing rate of events. Therefore research has focused on combining the advantages of conservative and optimistic algorithms creating so-called hybrid algorithms and on controlling the optimism in Time Warp. Such attempts to limit the optimism in Time Warp can be grouped into non-adaptive algorithms, adaptive algorithms with local state and adaptive algorithms with global state. Carl Tropper [34] and Samir R. Das [5] both give a good overview on algorithms in these categories. The group of non-adaptive algorithms for instance contains algorithms that use time windows in order to limit how far ahead of the current GVT single LPs can process their events, which limits the frequency and length of rollbacks. Other algorithms in this group add conservative ideas to Time Warp. One example for this is the Breathing Time Buckets algorithm (also known as SPEEDES algorithm) [30]. Like Time Warp this algorithm executes all local events immediately and performs local rollbacks if required but it only sends events to other LPs that have been guaranteed by the current GVT and by doing so avoids cascaded rollbacks. The problem of all these algorithms is that either the effectiveness depends on finding the optimum value for static parameters like the window size or conservative aspects of the algorithm limit its effectiveness for models with certain characteristics. Finding the optimum value for such control parameters can be difficult for simulation modellers and many simulation models show a very dynamic behaviour of their characteristics, which would require different parameters at different times of the simulation. Adaptive algorithms solve this problem by dynamically adapting the control parameters of the synchronisation algorithm according to “selected aspects of the state of the simulation” [24]. Some of these algorithms use mainly global state information like the Adaptive Memory Management algorithm [6], which uses the total amount of memory used by all LPs or the Near Perfect State Information algorithms [28] that are based on the availability of a reduced information set that almost perfectly describes the current global state of the simulation. Adaptive algorithms based on local state use only local information available to each LP in order to change the control parameters. They collect historic local state information and from these try to predict future local states and the required control parameter. Some examples for adaptive algorithms using local state
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: 12 Fundamental Concepts known as st
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
Page 67 and 68: 58 Implementation chain at the corr
Page 69 and 70: 60 Implementation The algorithm wil
Page 71 and 72: No Start Is Active Object body acti
Page 73 and 74:
5.3.2 GVT Calculation and End of Si
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
Page 85 and 86:
76 Validation of the Parallel Simul
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
6.4 Validation 4 84 Validation of t
Page 95 and 96:
Page 97 and 98:
… LpccEnabled=true LpccClusterNum
Page 99 and 100:
Validation 5.2 Validation of the Pa
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
7 Conclusions 96 Conclusions Even s
Page 107 and 108:
References 98 References [1] Amin K
Page 109 and 110:
[19] Krafft G. Parallelisation of T
Page 111 and 112:
Appendix A: Detailed GPSS Syntax 10
Page 113 and 114:
Reserved word: DEPART Syntax: [] DE
Page 115 and 116:
Reserved word: SEIZE Syntax: [] SEI
Page 117 and 118:
Setting: LpccClusterNumber Default
Page 119 and 120:
Appendix C: Simulator log4j loggers
Page 121 and 122:
Logger: parallelJavaGpssSimulator.l
Page 123 and 124:
114 Appendix D: Structure of the at
Page 125 and 126:
Appendix E: Documentation of select
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
!"#"$$%$&"'"(!))*+,-$"./#0$! 1$"))
Page 133 and 134:
Page 135 and 136:
!/+/.7/8$ Appendix E: Documentation
Page 137 and 138:
Returns: Appendix E: Documentation
Page 139 and 140:
!"#$%&''$()*)")$ +',)$-)$. #,/. !"#
Page 141 and 142:
!"#$%&'()#%*+,"-*.# Appendix E: Doc
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Constructor Detail !"#$%&'$(#$)*$%+
Page 155 and 156:
Page 157 and 158:
Validation 1.1, output of simulate:
Page 159 and 160:
Validation 2, output of LP: Appendi
Page 161 and 162:
Appendix F: Validation output logs
Page 163 and 164:
Validation 2, output of simulate: A
Page 165 and 166:
Validation 3.1, output of LP1: Appe
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Validation 4, output of LP1: Append
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
simulation for time 2000) (628) 20:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Validation 4, output of simulate: A
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
(135) 19:37:56,477 Total transactio
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
(68) 19:37:56,619 by the transactio
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
(1295) 14:25:22,740 Simulation stop
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
(68) 14:25:22,888 by the transactio
Page 239 and 240:
Validation 6.2, output of LP2: (1)
Page 241:
(32) (1,2) 0 765 Block: TRANSFER 0.
show all

3.1 MB - Evernote

Create successful ePaper yourself

Delete template?

Save as template?