Performance prediction for families of data-intensive software applications
conference paper
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.
TNO Identifier
785814
ISBN
978-1-4503-5629-9
Publisher
ACM
Source title
9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018), Berlin, Germany - April 9-13, 2018
Files
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