Control plane algorithms targeting challenging autonomic properties in grey systems

Autonomic self-configuration is an essential mechanism underpinning agile service provision, allowing predictive or non-predictive grey system domains to transform their underlying system behaviour. However, much more work is required to improve our understanding of the control plane algorithms for these systems to prevent challenging autonomic system properties from impacting service provisioning. Current research focuses upon three key ‘planes’; the knowledge plane, the control plane, and the data plane. This work proposes and presents a prototype for an autonomic self-configuration control framework, comprising of a Control Plane Control Protocol, and optional algorithmic overlay. The framework bridges the three networking planes, with the knowledge plane governance selecting the required configuration data, and the control plane controlling the negotiation and dissemination of the payload, through a generic control protocol, and an optional control plane overlay. The new framework can form part of a protective system infrastructure. A novel aspect of this framework is that it can address challenging autonomic properties through the control plan overlay. By autonomically controlling how a networked appliance responds to an external stimulus; a permanent or transitory change may ensue or self-configuration can be prevented. These framework attributes are evaluated using a prototype to demonstrate algorithms assessing actor hybridization and blocking avalanches of changes that may result in unrestrained, rapid actor hybridization.