Model-driven Self-optimization Using Integer Linear Programming and Pseudo-Boolean Optimization

The development of self-optimizing software systems usually requires developers to apply optimization techniques manually, which is time consuming and prone to error. The application of model-driven software development combined with models at runtime takes this burden from developers by generating optimization problems using model transformations. In this paper, we present two such approaches applying integer linear programming and pseudoboolean optimization. Furthermore, we provide a scalability analysis of both approaches showing their feasibility for pipeand-filter applications. Keywords—self-adaptive systems; integer linear programming; pseudo-boolean optimization; MDSD

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