Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
IEEE Transactions on Computers
The UltraSAN modeling environment
Performance Evaluation - Special issue: performance modeling tools
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
Linearizer: a heuristic algorithm for queueing network models of computing systems
Communications of the ACM
Analyzing queueing networks with simultaneous resource possession
Communications of the ACM
Performance validation tools for software/hardware systems
Performance Evaluation
A Unifying Framework for the Approximate Solution of Closed Multiclass Queuing Networks
IEEE Transactions on Computers
IEEE Transactions on Software Engineering
The MVA Pre-empt resume priority approximation
SIGMETRICS '83 Proceedings of the 1983 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Feedback Control with Queueing-Theoretic Prediction for Relative Delay Guarantees in Web Servers
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
Two-Level Iterative Queuing Modeling of Software Contention
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Simulation Model for Self-Adaptive Applications in Pervasive Computing
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
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SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Short term performance forecasting in enterprise systems
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SAP Performance Optimization Guide: Analyzing and Tuning SAP Systems
SAP Performance Optimization Guide: Analyzing and Tuning SAP Systems
Journal of Systems and Software
Performance Analysis Using Stochastic Petri Nets
IEEE Transactions on Computers
Service System Resource Management Based on a Tracked Layered Performance Model
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Enhanced Modeling and Solution of Layered Queueing Networks
IEEE Transactions on Software Engineering
JMT: performance engineering tools for system modeling
ACM SIGMETRICS Performance Evaluation Review
Engineering autonomic controllers for virtualized web applications
ICWE'10 Proceedings of the 10th international conference on Web engineering
Model-driven web engineering performance prediction with layered queue networks
ICWE'10 Proceedings of the 10th international conference on Current trends in web engineering
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
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Analytic performance models are being increasingly used to support system runtime optimization. This paper considers the modelling features needed to predict the response time behaviour of an industrial enterprise resource planning (ERP) application, SAP ERP. A number of studies have reported modelling success with the application of basic product-form Queueing Network Models (QNMs) to multi-tier systems. Such QNMs are often preferred in the context of optimization studies due to the low computational costs of their solution. However, we show that these simple models do not support many important features required to accurately characterize industrial applications such as ERP systems. Specifically, our results indicate that software threading levels, asynchronous database calls, priority scheduling, multiple phases of processing, and the parallelism offered by multi-core processors all have a significant impact on response time that cannot be neglected. Starting from these observations, the paper shows that Layered Queueing Models (LQMs) are a robust alternative to basic QNMs, while still enjoying analytical solution algorithms that facilitate their integration in optimization studies. A case study for a sales and distribution workload demonstrates that many of the features supported by LQMs are critical for achieving good prediction accuracy. Results show that, remarkably, all of the features we considered that are not captured by basic product-form QNMs are needed to predict mean response times to within 15% of measured values for a wide range of load levels. If any key feature is absent, the mean response time estimates could differ by 36% to 117% compared to the measured values, thus making the case that such non-product-form modelling features are needed for complex real-world applications.