Quantifying and predicting the influence of execution platform on software component performance:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Karlsruhe
KIT Scientific Publ.
2011
|
| Schriftenreihe: | The Karlsruhe series on software design and quality
5 |
| Schlagworte: | |
| Online-Zugang: | kostenfrei Inhaltsverzeichnis |
| Beschreibung: | Hergestellt on demand |
| Beschreibung: | XVII, 417 S. Ill., graph. Darst. 21 cm, 750 g |
| ISBN: | 9783866447417 3866447418 |
Internformat
MARC
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| 245 | 1 | 0 | |a Quantifying and predicting the influence of execution platform on software component performance |c by Michael Kuperberg |
| 264 | 1 | |a Karlsruhe |b KIT Scientific Publ. |c 2011 | |
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Datensatz im Suchindex
| _version_ | 1804151767640834048 |
|---|---|
| adam_text | CONTENTS
1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 PROBLEM STATEMENT AND SCIENTIFIC CHALLENGES 4
1.3 SHORTCOMINGS OF EXISTING SOLUTIONS 8
1.4 THESIS APPROACH 11
1.5 CONTRIBUTIONS 16
1.6 VALIDATION 18
1.7 THESIS ORGANISATION 19
2 FOUNDATIONS AND STATE-OF-THE-ART 21
2.1 SOFTWARE PERFORMANCE 21
2.2 PERFORMANCE EVALUATION, ENGINEERING, OPTIMISATION, MODELLING
AND PREDICTION 24
2.2.1 MODEL-BASED PERFORMANCE PREDICTION 25
2.2.2 SOFTWARE PERFORMANCE ENGINEERING 26
2.3 BENCHMARKING AND PERFORMANCE MEASURING 27
2.3.1 BENCHMARK TYPES 28
2.3.2 OVERVIEW OF BENCHMARKS 31
2.3.3 SUMMARY 32
2.4 AN OVERVIEW OF TIMER METHODS, TIMERS AND COUNTERS 33
2.4.1 HARDWARE PERFORMANCE COUNTERS AND MONITORS 34
2.4.2 SOFTWARE-PROVIDED PERFORMANCE INDICATORS 38
2.4.3 TIMER METHODS 39
2.4.4 SUMMARY 44
2.5 MIDDLEWARE, VIRTUAL MACHINES AND BYTECODE 45
BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/1017171491
DIGITALISIERT DURCH
CONTENTS
2.6 JUST-IN-TIME COMPILATION 47
2.7 BYTECODE ENGINEERING 50
2.8 INSTRUMENTATION 51
2.9 AHEAD-OF-TIME COMPILATION (AOT) 52
2.10 WORKLOAD QUANTIFICATION, RESOURCE DEMAND QUANTIFICATION AND
PROFILING 54
2.11 SOFTWARE COMPONENTS AND THEIR PERFORMANCE 56
2.11.1 COMPONENT BASICS 57
2.11.2 COMPONENT MODELLING 58
2.11.3 COMPONENT PERFORMANCE MODELLING 60
2.12 PLATFORM-INDEPENDENT RESOURCE DEMANDS 62
2.13 PALLADIO COMPONENT MODEL 63
2.13.1 COMPONENT MODELLING 65
2.13.2 EXECUTION PLATFORM AND SYSTEM USAGE MODELLING 67
2.14 QUANTITATIVE IMPACT OF JVM OPTIMIZATIONS 69
3 EVALUATING AND SELECTING METHODS FOR TIME MEASUREMENT 73
3.1 ISSUES AND CHALLENGES WITH OBTAINING TIMING VALUES FOR PERFORM-
ANCE ANALYSIS 74
3.2 FOUNDATIONS OF TIMER METHODS 76
3.2.1 QUALITY PROPERTIES FOR COUNTERS, TIMERS AND TIMER METHODS 79
3.2.2 THE INFLUENCE OF QUANTISATION, ACCURACY AND METHOD IN-
VOCATION COSTS ON MEASURED TIMING VALUES 82
3.2.3 THE EFFECTS OF ROUNDING AND TRUNCATING 85
3.3 QUANTIFYING ACCURACY AND INVOCATION COST OF TIMING METHODS ... 89
3.3.1 A NAIVE APPROACH TO ESTIMATING TIMER INVOCATION COSTS . . 89
3.3.2 USING CLUSTERING FOR QUANTIFYING ACCURACY AND INVOCATION
COST 91
3.3.3 TIMER METHOD INVOCATION IN DETAIL 98
3.4 ANALYSING UNITS, MONOTONICITY AND STABILITY 100
3.4.1 QUANTIFYING UNITS OF COUNTERS AND TIMERS 102
XN
CONTENTS
3.4.2 ANALYSING MONOTONICITY DURING CONCURRENT ACCESS TO TIM-
ING METHODS 105
3.4.3 ANALYSING STABILITY OF A TIMER 109
3.5 COMPUTING THE MAXIMUM MEASURABLE TIME INTERVAL AND THE EPOCHSLL3
3.5.1 FOUNDATIONS 115
3.5.2 IMPACT OF OVERFLOW ON TIMER METHODS WITH HIGH PRECISION .117
3.5.3 IMPACT OF OVERFLOW ON MEASURING TIME INTERVALS 119
3.5.4 COMPUTING THE LAST AND NEXT EPOCHS 121
3.6 A UNIFIED QUALITY METRIC FOR TIMER METHODS 122
3.6.1 ACCOUNTING FOR DIFFERENT CPU PROCESSING SPEEDS 123
3.6.2 FACTORS CONTRIBUTING TO THE UNIFIED TIMER QUALITY METRIC . . 123
3.6.3 DESIGNING THE UNIFIED TIMER QUALITY METRIC 125
3.6.4 CHOICE OF THE EXPONENTS FOR THE UNIFIED TIMER QUALITY METRIC 126
3.7 SUMMARY 128
4 QUANTIFYING RESOURCE DEMANDS FOR PERFORMANCE PREDICTION 131
4.1 TIMING VALUES VERSUS RESOURCE DEMANDS 133
4.1.1 EFFECTS ON PREEMPTION ON RESPONSE TIME MEASUREMENTS . . .134
4.1.2 ADDRESSING PREEMPTION DURING TIME MEASUREMENTS 134
4.1.3 RESOURCE DEMANDS 136
4.2 REQUIREMENTS FOR RESOURCE DEMAND USAGE IN THE PCM 138
4.3 USING JAVA BYTECODE FOR RESOURCE DEMAND QUANTIFICATION 140
4.3.1 FOUNDATIONS OF JAVA BYTECODE 141
4.3.2 BLACK-BOX JAVA BYTECODE 143
4.3.3 BYTECODE INSTRUCTIONS WITH SPECIAL ROLES AND PROPERTIES . . . 145
4.3.4 PARAMETERS OF BYTECODE INSTRUCTIONS 147
4.3.5 METHODS IN BYTECODE AND JAVA PLATFORM API 153
4.3.6 NATIVE METHODS IN JAVA BYTECODE 158
4.3.7 STATIC METHODS IN JAVA BYTECODE 160
4.3.8 WORKING WITH CALLING CONTEXT TREES 161
4.3.9 CONSIDERING SUBTREES OF CALLING CONTEXT TREES 166
4.3.10 USAGE OF PASSIVE RESOURCES FROM JAVA BYTECODE 168
XIN
CONTENTS
4.3.11 BYTECODE INSTRUCTION EQUIVALENCE CLASSES 170
4.4 USING TRANSPARENT APPLICATION INSTRUMENTATION FOR BYTECODE
COUNTING 171
4.4.1 REQUIREMENTS FOR THE INSTRUMENTATION PROCESS 173
4.4.2 EVALUATING AND STORING COUNTING RESULTS 176
4.4.3 ANALYSIS OF BYTECODE INVARIANTS AND BASIC BLOCKS 178
4.4.4 INSERTING BYTECODE INFRASTRUCTURE FOR RUNTIME COUNTING . . . 182
4.4.5 QUANTIFYING THE IMPACT OF THE INSTRUMENTATION 184
4.4.6 RECORDING CALLING CONTEXT DETAILS 187
4.4.7 REPORTING AND AGGREGATING COUNTING RESULTS 188
4.5 ASSUMPTIONS AND LIMITATIONS 190
4.6 SUMMARY 190
5 BENCHMARKING THE JVM OPERATIONS FOR PERFORMANCE PREDICTION 193
5.1 CHALLENGES OF TRANSLATING RESOURCE DEMANDS INTO TIMING VALUES . 194
5.2 BYTECODE INSTRUCTION BENCHMARKING 196
5.2.1 UNSUITABILITY OF SOURCE CODE FOR BYTECODE INSTRUCTION
BENCHMARKING 200
5.2.2 UNSUITABILITY OF KERNEL COLLECTIONS FOR BYTECODE INSTRUCTION
BENCHMARKING 201
5.2.3 ATTEMPTING TO MEASURE BYTECODE INSTRUCTIONS USING BYTE-
CODE ENGINEERING 203
5.2.4 ATTEMPTING TO CREATE BYTECODE BENCHMARKS RANDOMLY . . . 206
5.2.5 PRECONDITIONS AND POSTCONDITIONS OF BYTECODE INSTRUCTIONS . 208
5.2.6 BYTECODE BENCHMARKING SCENARIOS 211
5.2.7 OVERVIEW OF SCENARIO-DRIVEN AUTOMATED BYTECODE BENCH-
MARKING 215
5.3 METHOD AND API BENCHMARKING 217
5.3.1 SCIENTIFIC CHALLENGES 218
5.3.2 FOUNDATIONS 221
5.3.3 OVERVIEW OF THE APLBENCHJ FRAMEWORK 224
5.3.4 SATISFYING PRECONDITIONS USING HEURISTICS 227
XIV
CONTENTS
5.3.5 HEURISTIC EXCEPTION HANDLER 234
5.3.6 GENERATING AND EXECUTING MICROBENCHMARKS 242
6 PERFORMANCE PREDICTION AND PCM INTEGRATION 247
6.1 COMPUTING THE PREDICTED EXECUTION DURATION 249
6.1.1 SELECTING THE INPUT FOR PREDICTION CALIBRATION 250
6.1.2 COMPUTING THE CALIBRATION FACTOR 252
6.2 INTEGRATION INTO THE PALLADIO COMPONENT MODEL 256
6.2.1 EXISTING RESOURCE DEMAND MODELLING IN THE PCM 256
6.2.2 BYTECODE-BASED PERFORMANCE PREDICTION: UNSUITABILITY OF
EXISTING PCM RESOURCE MODELLING 258
6.2.3 SCENARIOS AND REQUIREMENTS FOR EXTENDING THE PCM META-
MODEL 261
6.2.4 EXTENSIONS OF THE PCM METAMODEL 263
6.2.5 MODELLING THE JVM AND THE BYTECODE COMPONENTS 268
6.2.6 REPRESENTING JVM INSTRUCTIONS AND METHODS AS RESOURCE
SERVICES 269
6.2.7 EXPRESSING THE PLATFORM-SPECIFIC NATURE OF JVM BENCH-
MARKING RESULTS 271
6.2.8 MODELLING THE CALIBRATION FACTOR 273
6.3 SUMMARY 276
7 VALIDATION 277
7.1 BYTECODE-BASED PERFORMANCE PREDICTION 277
7.1.1 VALIDATION OVERVIEW 278
7.1.2 SUBJECTS AND SCENARIOS FOR THE VALIDATION 280
7.1.3 PERFORMANCE PREDICTION: GOALS, QUESTIONS AND METRICS .... 282
7.1.4 PERFORMANCE PREDICTION: RESULTS OF VALIDATION 284
7.1.5 RESOURCE DEMAND QUANTIFICATION: GOALS, QUESTIONS AND
METRICS FOR VALIDATION 299
7.1.6 RESOURCE DEMAND QUANTIFICATION: VALIDATION RESULTS 300
XV
CONTENTS
7.1.7 EXECUTION PLATFORM BENCHMARKING: GOALS, QUESTIONS AND
METRICS FOR VALIDATION 306
7.1.8 EXECUTION PLATFORM BENCHMARKING: VALIDATION RESULTS .... 307
7.1.9 SUMMARY AND DISCUSSION 313
7.2 TIMER EVALUATION 315
7.2.1 STABILITY AND MONOTONICITY 318
7.2.2 UNITS: COMPUTING AND VERIFYING 323
7.2.3 ACCURACY, INVOCATION COST AND INVOCATION COST SPREAD .... 327
7.2.4 EFFECT OF JUST-IN-TIME COMPILATION ON TIMER METHODS 333
7.2.5 EPOCHS AND MAXIMUM MEASURABLE TIME INTERVALS 335
7.2.6 UNIFIED TIMER QUALITY METRIC 338
7.2.7 SUMMARY AND DISCUSSION 339
8 RELATED WORK 343
8.1 TIMER METHODS 343
8.2 RUNTIME COUNTING OF EXECUTED BYTECODE INSTRUCTIONS AND METHOD
INVOCATIONS 345
8.3 JVM BENCHMARKING 349
8.4 PERFORMANCE PREDICTION 353
8.4.1 COMPONENT-BASED PERFORMANCE PREDICTION AND ENGINEERING 353
8.4.2 BYTECODE-BASED PERFORMANCE PREDICTION 354
8.4.3 CROSS-PLATFORM PERFORMANCE PREDICTION 355
8.5 RESOURCE AND EXECUTION PLATFORM MODELLING IN COMPONENT
METAMODELS 357
9 CONCLUSION 361
9.1 SUMMARY 361
9.2 FUTURE WORK 366
9.2.1 BYTECODE-BASED RESOURCE DEMAND QUANTIFICATION 366
9.2.2 BENCHMARKING OF THE JAVA VIRTUAL MACHINE 368
9.2.3 TIMER METHODS AND PERFORMANCE INDICATORS 371
9.2.4 RESOURCE MODELLING AND PALLADIO COMPONENT MODEL 371
XVI
CONTENTS
A APPENDIX 373
A.L PERFORMANCE EQUIVALENCE CLASSES OF JAVA BYTECODE INSTRUCTIONS . .373
B LIST OF FIGURES 377
C LIST OF TABLES 381
D LISTINGS 383
BIBLIOGRAPHY 384
XVII
|
| any_adam_object | 1 |
| author | Kuperberg, Michael |
| author_facet | Kuperberg, Michael |
| author_role | aut |
| author_sort | Kuperberg, Michael |
| author_variant | m k mk |
| building | Verbundindex |
| bvnumber | BV041579356 |
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| collection | ebook |
| ctrlnum | (OCoLC)762170484 (DE-599)DNB1017171491 |
| dewey-full | 005.12 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.12 |
| dewey-search | 005.12 |
| dewey-sort | 15.12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Thesis Book |
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| indexdate | 2024-07-10T01:00:03Z |
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| isbn | 9783866447417 3866447418 |
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| series | The Karlsruhe series on software design and quality |
| series2 | The Karlsruhe series on software design and quality |
| spelling | Kuperberg, Michael Verfasser aut Quantifying and predicting the influence of execution platform on software component performance by Michael Kuperberg Karlsruhe KIT Scientific Publ. 2011 XVII, 417 S. Ill., graph. Darst. 21 cm, 750 g txt rdacontent n rdamedia nc rdacarrier The Karlsruhe series on software design and quality 5 Hergestellt on demand Zugl.: Karlsruhe, Karlsruher Inst. für Technologie, Diss., 2010 Leistungsbewertung (DE-588)4167271-9 gnd rswk-swf Modellgetriebene Entwicklung (DE-588)4832365-2 gnd rswk-swf Komponente Software (DE-588)4439521-8 gnd rswk-swf Java Virtual Machine (DE-588)4541748-9 gnd rswk-swf Byte-Code (DE-588)4702178-0 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Komponente Software (DE-588)4439521-8 s Modellgetriebene Entwicklung (DE-588)4832365-2 s Leistungsbewertung (DE-588)4167271-9 s Java Virtual Machine (DE-588)4541748-9 s Byte-Code (DE-588)4702178-0 s DE-604 The Karlsruhe series on software design and quality 5 (DE-604)BV041745603 5 http://digbib.ubka.uni-karlsruhe.de/volltexte/1000023351 kostenfrei Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027024642&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kuperberg, Michael Quantifying and predicting the influence of execution platform on software component performance The Karlsruhe series on software design and quality Leistungsbewertung (DE-588)4167271-9 gnd Modellgetriebene Entwicklung (DE-588)4832365-2 gnd Komponente Software (DE-588)4439521-8 gnd Java Virtual Machine (DE-588)4541748-9 gnd Byte-Code (DE-588)4702178-0 gnd |
| subject_GND | (DE-588)4167271-9 (DE-588)4832365-2 (DE-588)4439521-8 (DE-588)4541748-9 (DE-588)4702178-0 (DE-588)4113937-9 |
| title | Quantifying and predicting the influence of execution platform on software component performance |
| title_auth | Quantifying and predicting the influence of execution platform on software component performance |
| title_exact_search | Quantifying and predicting the influence of execution platform on software component performance |
| title_full | Quantifying and predicting the influence of execution platform on software component performance by Michael Kuperberg |
| title_fullStr | Quantifying and predicting the influence of execution platform on software component performance by Michael Kuperberg |
| title_full_unstemmed | Quantifying and predicting the influence of execution platform on software component performance by Michael Kuperberg |
| title_short | Quantifying and predicting the influence of execution platform on software component performance |
| title_sort | quantifying and predicting the influence of execution platform on software component performance |
| topic | Leistungsbewertung (DE-588)4167271-9 gnd Modellgetriebene Entwicklung (DE-588)4832365-2 gnd Komponente Software (DE-588)4439521-8 gnd Java Virtual Machine (DE-588)4541748-9 gnd Byte-Code (DE-588)4702178-0 gnd |
| topic_facet | Leistungsbewertung Modellgetriebene Entwicklung Komponente Software Java Virtual Machine Byte-Code Hochschulschrift |
| url | http://digbib.ubka.uni-karlsruhe.de/volltexte/1000023351 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027024642&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV041745603 |
| work_keys_str_mv | AT kuperbergmichael quantifyingandpredictingtheinfluenceofexecutionplatformonsoftwarecomponentperformance |