Driven by an ever-increasing diversity of application contexts, execution environments and scalability requirements, modern software is faced with the challenge of frequent code refactoring. To address this, we have proposed an STL-like self-adaptive container library, which dynamically changes its data structures and resource usage to meet programmer-specified Service Level Objectives relating to performance, reliability and primary memory use. A prototype of this library has been implemented and utilised in two case studies to prove its viability. In the present work, we explore a low-cost means to extend our library to satisfy wider classes of Service Level Objectives. This is achieved through the integration of third-party container frameworks, which exploit parallelism to boost performance and disk-based data offloading to reduce primary memory consumption, and the integration of cloud storage services, which offer cost-effective location-free storage. We demonstrate our library's application in a state-space exploration case study. With very low programmer overhead, experimental results show that our library can improve performance with a 76% reduction in insertion time and an 86% reduction in search time, and can also exploit out-of-core storage, including cloud storage.