# An Uncertainty-Driven Proactive Self-Healing Model for
###### Abstract  
The ever-increasing demand for services of end-users in the Internet of Things (IoT) often causes great congestion in the nodes dedicated to serving their requests. Such nodes are usually placed at the edge of the network, becoming the intermediates between the IoT infrastructure and Cloud. Edge nodes offer many advantages when adopted to perform processing activities that are realized close to end-users, limiting the latency in the provision of responses. In this article, we attempt to solve the problem of the potential overloading of edge nodes by proposing a mechanism that always keeps free space in their queue to host high-priority processing tasks. We introduce a proactive, self-healing mechanism that utilizes the principles of Fuzzy Logic, in combination with a non-parametric statistical method that reveals the trend of nodes' loads as depicted by the incoming tasks and their capability to serve them in the minimum possible time. Through our approach, we manage to ensure the uninterrupted service of high-priority tasks, taking into consideration the demand for tasks as well. Based on this approach, we ensure the fastest possible delivery of results to the requestors while keeping the latency for serving high-priority tasks at the lowest possible levels. A set of experimental scenarios is adopted to evaluate the performance of the suggested model by presenting the corresponding numerical results. self-healing systems; task management; pervasive computing; Fuzzy Logic; task offloading 2022  
the latency that high-priority tasks may face in overloaded nodes. The proposed model is strategically chosen to cover any type of network. Our model does not focus on the network level, but it is node oriented. We look at the individual behavior of the node, empowering it to cover the uncertainty when it receives tasks and tries to handle them. In our case, we are referring to a higher level since we are not looking at network characteristics. We care about maintaining self-healing in the dimension of QoS at a high level and in this sense, the self-healing that we do is transparent. One can find multiple efforts in past studies that try to solve the overloading problem mainly focusing on activities performed in a network. For instance, in [2], the authors study an M/M/1 queue subject to alternative behaviors. They assume that the properties of the queue fluctuate randomly over time. The authors also consider two operational conditions which allow the modelling of queues based on two modes of arrivals with variable high-low rates. If the current environment leads to a traffic congestion, the switch to the other environment (i.e., operational condition) may yield a favorable consequence for the length of the queue, achieving a stable system. In [3], a self-healing approach is proposed in order to maintain connectivity in reconfigurable networks. In this paper, the case of the network being compromised by an attack is studied. The suggested distributed algorithm deletes the affected node and reconfigures the network structure to maintain its functionality. This approach, however, is limited only to reconfigurable networks, in contrast to the approach we propose, which can be functional no matter the type of network and the status of edge nodes. Both refs. [4, 5] focus on a task offloading approach to manage the high load in mobile devices by offloading computational and heavy tasks to nearby edge nodes. This way, the presented model aims at reducing the task processing delay and minimizing the overall service time for latency-sensitive (IoT) applications. In [6], the authors introduce a framework in which the tasks with high computing requirements are offloaded from the end devices to the edge nodes. An intelligent task offloading scheme that generates the corresponding offloading decision profiles in varying scenarios is presented in [7]. This scheme combines machine learning and a learning-based offloading strategy based on historical data.