A failure detector based on processes’ relevance and the confidence degree in the system for self-healing in ubiquitous environments

Ubiquitous systems have become a common technology in our everyday lives, thus increasing our dependency on them. However, the occurrence of failures in these types of systems can reduce their applicability/usability which may result in some difficult, or even dangerous, consequences. Such systems must, therefore, possess self-healing capabilities in order to detect failures and make the necessary adjustments to prevent their impact on applications. In such a context, this work proposes a new and flexible unreliable failure detector (FD), denoted as the Impact failure detector, which is based on process relevance and the confidence degree in the system, for self-healing systems in ubiquitous environments. The output of the Impact FD concerns the confidence in the system as a whole. By expressing the relevance of each node by an impact factor value as well as a margin of acceptable failures of the system, the Impact FD enables the user to tune the failure detection configuration in accordance with the requirements of the application. The performance evaluation results confirm the degree of flexible applicability of our FD and, that due to the margin of failure, the number of false responses may be reduced when it is compared with traditional unreliable FDs.