Performance Modeling and Benchmarking of Event-Based ... - DVS

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priorities. Finally, we validated our modeling methodology to model EBS in two case studies and predicted system behavior and performance under load successfully. As part of the first case study we extended SIENA, a well-known distributed EBS, with a runtime measurement framework and predicted the runtime behavior including delivery latency for a basic workload. In the second case study, we developed a comprehensive model of the complete SPECjms2007 workload. To model the workload we applied our performance modeling patterns as well as our QPN extensions. We considered a number of different scenarios with varying workload intensity (up to 4,500 transaction / 30,000 messages per second) and compared the model predictions against measurements. The results demonstrated the effectiveness and practicality of the proposed modeling and prediction methodology in the context of a real-world scenario. This thesis opens up new avenues of frontier research in the area of event-based systems. Our performance modeling methodology can be used to build self-adaptive EBS using automatic model extraction techniques. Such systems could dynamically adjust their configuration to ensure that QoS requirements are continuously met.
Contents 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Approach and Contributions of This Thesis . . . . . . . . . . . . . . . . . . . . . 2 1.3.1 Contributions in Performance Engineering . . . . . . . . . . . . . . . . . . 3 1.3.2 Contributions in the Area of Benchmarking . . . . . . . . . . . . . . . . . 4 1.3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.4 Related Activities and Publications . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Background 9 2.1 Event-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Technology Platforms of Event-based Systems . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Active Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.2 Continuous Queries, Stream Processing . . . . . . . . . . . . . . . . . . . 13 2.2.3 Materialized Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.4 Message-Oriented Middleware (MOM) . . . . . . . . . . . . . . . . . . . . 13 2.2.5 Distributed Event-Based Systems (DEBS) . . . . . . . . . . . . . . . . . . 18 2.2.6 Reactive Middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Introduction to Queueing Petri Nets . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3 Related Work 25 3.1 Performance Modeling of Event-Based Systems . . . . . . . . . . . . . . . . . . . 25 3.2 Benchmarking of Event-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Patterns in Performance Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4 Performance Engineering of Event-Based Systems 33 4.1 Modeling Methodology for EBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.1 Formal Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.2 Analysis of the Event Routing Behavior . . . . . . . . . . . . . . . . . . . 36 4.1.3 Estimation of Event Service Times . . . . . . . . . . . . . . . . . . . . . . 37 4.1.4 System Operational Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.1.5 Performance Model Construction and Evaluation . . . . . . . . . . . . . . 40 4.2 Performance Modeling Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3 Extensions of QPNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.3.1 Mapping of Logical to Physical Resources . . . . . . . . . . . . . . . . . . 73 4.3.2 Non-Constant Cardinalities of Transitions . . . . . . . . . . . . . . . . . . 74 4.3.3 Priority of Transitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 iii
Page 1: Performance Modeling and Benchmarki
Page 6 and 7: 4.3.4 Tool Extension . . . . . . .
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Page 13 and 14: Acronym SPEC-OSG SPE SPN SQL ST SUT
Page 15 and 16: Chapter 1 Introduction 1.1 Motivati
Page 17 and 18: PC Server 310 PC Server 310 PC Serv
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4.1. MODELING METHODOLOGY FOR EBS 4
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4.3. EXTENSIONS OF QPNS 73 ,-).$%&'
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4.4. CONCLUDING REMARKS 75 Without
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Chapter 5 Benchmarking of Event-Bas
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5.1. SPECJMS2007 - A STANDARD BENCH
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5.2. CASE STUDY I: SPECJMS2007 101
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5.3. JMS2009-PS - A PUBLISH /SUBSCR
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5.4. CASE STUDY II: JMS2009-PS 115
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5.4. CASE STUDY II: JMS2009-PS 117
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Chapter 6 Performance Modeling of E
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6.2. MODELING SPECJMS2007 121 Scena
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6.2. MODELING SPECJMS2007 123 BASE*
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6.2. MODELING SPECJMS2007 125 1‘O
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6.2. MODELING SPECJMS2007 127 Sc. 1
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6.2. MODELING SPECJMS2007 129 Perce
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6.3. CONCLUDING REMARKS 133 the mod
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Chapter 7 Conclusions and Outlook W
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7.1. ONGOING AND FUTURE WORK 137 JA
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7.1. ONGOING AND FUTURE WORK 139 th
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Bibliography [1] J. Abbott, K. B. M
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BIBLIOGRAPHY 143 [29] BiCEP Researc
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BIBLIOGRAPHY 145 [61] G. Cugola and
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BIBLIOGRAPHY 147 [92] R. Henjes, M.
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BIBLIOGRAPHY 149 [128] S. Kounev an
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BIBLIOGRAPHY 151 [162] Object Compu
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BIBLIOGRAPHY 153 [197] K. Sachs, S.
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BIBLIOGRAPHY 155 [228] P. Tran, P.
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Erklärung Hiermit erkläre ich, di
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