On the predictive performance of queueing network models for large-scale distributed transaction processing systems

Abstract

Automated business processes running on distributed transaction processing (DTP) systems characterize the IT backbone of services industries. New web services standards such as BPEL have increased the importance of DTP systems in business practice. IT departments are forced to meet pre-defined quality-of-service metrics, therefore performance prediction is essential.Unfortunately, the complexity of multiple interacting services running on multiple hardware resources as well as the volatility in the demand for these services can make performance analysis extremely difficult. While business process automation has been a dominant topic in the recent years, surprisingly little has been published on performance modelling of large-scale DTP systems.In this paper, we will describe these systems with respect to the workloads and technical features, and compare the predictive accuracy of different types of queueing models and discrete event simulations experimentally. The experiments are based on two real-world DTP systems and respective data sets of a telecom company. Overall, we found that while the results for average utilization scenarios are quite similar, the effort to implement and run analytic solutions is much lower. As long as standard distributional assumptions of analytical models hold, they provide a reliable and fast methodology to explore different demand mix scenarios even for large-scale systems. The difficulty to estimate service and arrival time parameters and demand mix for the respective queueing network models can largely be reduced with appropriate tooling. Often, this information is missing in IT departments. Also, complex event conditions and error handling in DTP systems can make the analysis difficult. For many DTP applications, however, performance modelling could provide valuable decision support for service level management.

Short-term performance management by priority-based queueing

“…We then compared the predictions with the real-world response times calculated directly from the log files of the DTP systems. The predictive accuracy for all three models for System A was within 3% of the actual values, with a single outlier of around 11% [14]. For System B, the predictive accuracy was within 15%.…”

Autonomic Prioritization of Enterprise Transactions Based on Bid-Price Controls

“…In the literature, queuing theory is nearly exclusively used to model the response time behavior of multi-tiered applications and IT service infrastructures [12]- [13]. Simulations based on queuing theory are also used in this work to model the behavior of the online DTP systems under study.…”