Biblio

Performance is a critical system property of any system, in particular of data-intensive systems, such as image processing systems. We describe a performance engineering method for families of data-intensive systems that is both simple and accurate; the performance of new family members is predicted using models of existing family members. The predictive models are calibrated using static code analysis and regression. Code analysis is used to extract performance profiles, which are used in combination with regression to derive predictive performance models. A case study presents the application for an industrial image processing case, which revealed as benefits the easy application and identification of code performance optimization points. 

There is a tendency to move production environments from corporate-owned data centers to cloud-based services. Users who do not maintain a private production environment might not wish to maintain a private performance test environment either. The application of performance engineering methods to the development and delivery of software systems is complicated when the form and or parameters of the target deployment environment cannot be controlled or determined. The difficulty of diagnosing the causes of performance issues during testing or production may be increased by the presence of highly variable workloads on the target platform that compete with the application of interest for resources in ways that might be hard to determine. In particular, performance tests might be conducted in virtualized environments that introduce factors influencing customer-affecting metrics (such as transaction response time) and observed resource usage. Observed resource usage metrics in virtualized environments can have different meanings from those in a native environment. Virtual machines may suffer delays in execution. We explore factors that exacerbate these complications. We argue that these complexities reinforce the case for rigorously using software performance engineering methods rather than diminishing it. We also explore possible performance testing methods for mitigating the risk associated with these complexities.