Database and model queries are the foundations of data-driven applications. Their performance is of primary importance in model-driven software engineering (MDE), especially with the evergrowing complexity of software modeling projects. To address the scalability issues of traditional MDE tools, distributed frameworks are being developed - however, query optimization in a distributed context brings a whole new set of challenges, including capacity limits of individual nodes and network communication. In this paper, we aim to address the allocation optimization challenge in the context of distributed incremental query evaluation. Our methods are based on a combination of heuristics-based resource consumption estimations and constraint satisfaction programming. We evaluate the impact of the optimization techniques by conducting benchmark measurements.
|Number of pages||6|
|Publication status||Published - 2015|
ASJC Scopus subject areas
- Computer Science(all)