On the building of self-adaptable systems to efficiently manage medical data

The systems that meet non-functional requirements (NFRs) are key for e-health services to face up events such as service outages and violations of confidentiality. However, traditional NFR systems produce overhead in execution time, which could affect critical decision-making processes. This paper presents a dynamic parallel pattern construction model to design and create efficient NFR self-adaptable systems. The construction of patterns is performed in two design phases: in the first one, the designers build NFR systems by creating pipelines including as many applications as required to meet the NFRs established by healthcare organizations. In the second phase, a pipeline is converted into a worker that automatically is added to a dynamic pattern. In a dynamic pattern, the workers can be cloned to be executed by different parallel patterns (e.g., manager/worker, divide&conquer, etc.) to face changes in the incoming workload during execution time, which converts a worker into a self-adaptable NFR system. A prototype was implemented to create self-adaptable NFR systems, which were used in a case study to manage spirometry studies, tomography images, and electrocardiograms. The evaluation showed the effectiveness of this dynamic pattern model to create self-adaptable systems when processing multiple types of medical data/contents. The evaluation also revealed that the self-adaptable NFR systems built by dynamic patterns yielded significant performance gain in a direct comparison with the implementation of NFR application pipelines built by a traditional framework called Jenkins.