Modern network applications require high performance and consume a lot of energy. Their inherent dynamic nature makes the dynamic memory subsystem a critical contributing factor to the overall energy consumption and to the execution time performance. This paper presents a novel, systematic methodology for generating performance-energy trade-offs by implementing optimal Dynamic Data Types, finely tuned and refined for network applications. Our systematic methodology is supported by a new, fully automated tool. We assess the effectiveness of the proposed approach in four representative, real-life case studies and provide significant energy savings and performance improvements compared to the original implementations.