Performance prediction for families of data-intensive software applications

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

Performance prediction for families of data-intensive software applications

Partial Networking, as a mechanism for moving-to-sleep and waking-up embedded systems, is beneficial for saving energy within a vehicle (or within other complex distributed systems). Even though a number of models exist which identify benefits of partial networking, they often address rather specific cases. Moreover, these fragmented efforts do not necessarily make explicit which methodological steps were taken. Explicating and analysing methodologies of existing research is beneficial to construct an overarching structure how to estimate potential energy savings for partial networking implementations. This structure can be used to select which steps to take to investigate the savings, and how to construct an argument for presenting the findings. This paper describes initial results of such a research. It reviews several models, illuminates their (sometimes not explicitly documented) methods, and outlines a generalized sequence for estimating partial networking benefits. Besides, it provides a list of questions to consider when introducing partial networking. The outlined methods and the analysis can be of interest to other domains interested in energy savings, such as smart grids, smart cities, and internet of things