Data of the Paper: Analytical Modeling and Empirical Validation of Performability of Service- and Cloud-Based Dynamic Routing Architecture Patterns

The online artifacts for the following article submitted to IEEE Transactions of Services Computing (2022): "Analytical Modeling and Empirical Validation of Performability of Service- and Cloud-Based Dynamic Routing Architecture Patterns" Abstract: Many dynamic routing architecture patterns are available including distributed routing, e.g., using the sidecar pattern, or centralized routing, e.g., using event stores or service buses. Different quality of service factors influence routing scheme and technology selection, such as performance, reliability, scalability and control properties offered by the patterns. An analytical model can formalize quality of service factors and facilitate the architectural decision making when changing the routing scheme, i.e., to a more distributed or centralized one. So far, the impact of these architecture patterns on performability, i.e., the overall performance of a system with impeded reliability, has not been extensively studied. This is important because making a decision to increase performance, e.g., by parallel processing of requests, may lead to decrease of reliability because of the added points of crash. We propose an analytical performability model of throughput during component crashes. For the empirical validation of our proposed model, we ran an extensive experiment of 2412 hours of runtime on a private cloud infrastructure and Google Cloud Platform. The very low prediction error of 0.57% indicates high accuracy of our analytical performability model. These results provide important new insights when making architectural decisions regarding service- and cloud-based dynamic routing.