Considering Transient Effects of Self-Adaptations in Model-Driven Performance Analyses

Stier, Christian and Koziolek, Anne
2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)
Model-driven performance engineering allows software architects to reason on performance characteristics of a software system in early design phases. In recent years, model-driven analysis techniques have been developed to evaluate performance characteristics of self-adaptive software systems. These techniques aim to reason on the ability of a self-adaptive software system to fulfill performance requirements in transient phases. A transient phase is the interval in which the behavior of the system changes, e.g., due to a burst in user requests. However, the effectiveness and efficiency with which a system is able to adapt depends not only on the time when it triggers adaptation actions but also on the time at which they are completed. Executing an adaptation action can cause additional stress on the adapted system. This can further impede the performance of the system in the transient phase. Model-driven analyses of self-adaptive software do not consider these transient effects. This paper outlines an approach for evaluating transient effects in model-driven analyses of self-adaptive software systems. The evaluation applied our approach to a horizontally scaling media hosting application in three experiments. By considering the delay in booting new Virtual Machines (VMs), we were able to improve the accuracy of predicted response times. The second and third experiment demonstrated that the increased accuracy enables an early detection and resolution of design deficiencies of self-adaptive software systems.
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