Autonomic computing was intended to tackle the growing complexity of Information Technology infrastructure by making it self-managing and self-adaptive. The core idea is to endow the system with enough intelligence to monitor continu- ously all aspects of the changing environments and resources, and to control management decisions according to high-level policies. For several years, great efforts have been devoted to the study of system performance, security, and fault management issues, but without much attention paid to self-adaptive social-collaborative system development. This may be because it is difficult to create such autonomic systems, which must sense and adapt to ongoing social context changes and support cyber-physical collaborations with minimal human involvement. These collaborations will have interactions between human and non-human entities that need to be self-managing with adaptive goals. This paper tackles the problem by introducing a new Generic Autonomic Social-Collaborative Framework (GASCF). It focuses on a high-level social-context based self-adaptive system, and its use of intelligent agents called autonomic adapters(AAs) that are driven by predefined policies. The paper describes the architecture of autonomic adapters and the general represen- tation of policies. It explores the effectiveness of the approach by applying it to a large-scale collaborative healthcare service called GRaCE (https://www.egrist.org/) that supports mental- health within the United Kingdom National Health Service and other organisations.
2018 International Conference on Intelligent Systems and Computer Vision (ISCV), IEEE