Policy based generic autonomic adapter for a context-aware social-collaborative system

Abstract

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 continuously 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 representation 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.