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

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132 CHAPTER 6. PERFORMANCE MODELING OF EBS - CASE STUDIES Avg. Completion Time (ms) 30 25 20 15 10 5 0 Model Measured 1 2 3 4 5 6 7 Avg. 1 2 3 4 5 6 7 Avg. Interactions (Medium Load) Interactions (High Load) Messages per Second 10000 8000 6000 4000 2000 P / T sent P / T received NP / NT sent NP / NT received 0 1 2 3 4 5 6 7 Total 1 2 3 4 5 6 7 Total Interactions (Medium Load) Interactions (High Load) (a) Scenario 1 Avg. Completion Time (ms) 4 3.5 3 2.5 2 1.5 1 0.5 Model Measured Avg. Completion Time (ms) 7 6 5 4 3 2 1 Model Measured 0 4 5 Avg. 4 5 Avg. 0 3 6 7 Avg. 3 6 7 Avg. Interactions (Medium Load) Interactions (High Load) Medium Load High Load Messages Per Second 6000 5000 4000 3000 2000 1000 P / T sent P / T received NP / NT sent NP / NT received Messages Per Second 25000 20000 15000 10000 5000 P / T sent P / T received NP / NT sent NP / NT received 0 4 5 Total 4 5 Total Interactions (Medium Load) Interactions (High Load) 0 3 6 7 Total 3 6 7 Total Medium Load High Load (b) Scenario 2 (c) Scenario 3 Figure 6.9: Model Predictions Compared to Measurements for Scenarios 1, 2 and 3
6.3. CONCLUDING REMARKS 133 the model is optimistic in that the predicted completion times are lower than the measured ones. This behavior is typical for performance models in general since no matter how representative they are, they normally cannot capture all factors causing delays in the system. 6.2.4 Conclusion In summary, the model proved to be very accurate in predicting the system performance, especially considering the size and complexity of the system that was modeled. The proposed model can be used as a powerful tool in the software engineering lifecycle of event-driven systems. For example at system design time, predictive performance models can be exploited for comparing alternative system designs with different communication and messaging patterns. At system deployment time, models help to detect system bottlenecks and to ensure that sufficient resources are allocated to meet performance and QoS requirements. 6.3 Concluding Remarks In this chapter, we presented two novel case studies of representative state-of-the-art event-based systems showing how our proposed methodology can be exploited for workload characterization, performance modeling and prediction. In the first case study, we applied our performance methodology to the SIENA publish/subscribe system and validated our approach by providing a workload characterization and performance model for a basic workload. In the second case study, we studied the SPECjms2007 standard benchmark deployed on a leading commercial middleware platform. A detailed model was developed in a step-by-step fashion and ways to customize the model for a particular deployment scenario were demonstrated. The model contains a total of 59 queueing places, 76 token colors and 68 transitions with a total of 285 firing modes. To validate our modeling technique we considered a real-life deployment in a representative environment comparing the model predictions against measurements on the real system. A number of different scenarios with varying workload intensity and interaction mixes were considered and the accuracy of the developed models was evaluated. The results demonstrated the effectiveness and practicality of the proposed modeling and prediction approach. The technique can be exploited as a powerful tool for performance prediction and capacity planning during the software engineering lifecycle of message-oriented event-driven systems.
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Performance Modeling and Benchmarki
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priorities. Finally, we validated o
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4.3.4 Tool Extension . . . . . . .
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5.7 Proportions of the Interactions
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List of Acronyms Acronym λ t,k j A
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Acronym SPEC-OSG SPE SPN SQL ST SUT
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Chapter 1 Introduction 1.1 Motivati
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PC Server 310 PC Server 310 PC Serv
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1.3. APPROACH AND CONTRIBUTIONS OF
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1.4. THESIS ORGANIZATION 7 • Stan
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Chapter 2 Background In this chapte
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2.1. EVENT-BASED SYSTEMS 11 is gene
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2.2. TECHNOLOGY PLATFORMS OF EVENT-
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2.3. INTRODUCTION TO QUEUEING PETRI
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2.3. INTRODUCTION TO QUEUEING PETRI
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Chapter 3 Related Work In this chap
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3.2. BENCHMARKING OF EVENT-BASED SY
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3.2. BENCHMARKING OF EVENT-BASED SY
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3.4. CONCLUDING REMARKS 31 hierarch
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Chapter 4 Performance Engineering o
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4.1. MODELING METHODOLOGY FOR EBS 3
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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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Page 169 and 170: BIBLIOGRAPHY 155 [228] P. Tran, P.
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