Fuzzy Q-learning approach for autonomic resource provisioning of IoT applications in fog computing environments

The dramatic growth of smart devices and the Internet of things (IoT) has increased the volume of exchanges and data on the web. The centralized and traditional architecture of cloud computing does not meet the demands of users and proper implementation of latency-sensitive applications due to latency and mass demands of IoT applications that have different needs compared to existing applications. As a result, edge computing has been presented for collecting and processing of data generated by these objects, which facilitates data processing with low latency and close to users at the edge of the network. Its main purpose is to bring computational resources and storage close to the end-user on the network. As far as the storage capacity of fog nodes is limited, the proper use of fog node resources significantly influences their performance. In this paper, a framework based on control MAPE-K loop has been used to obtain the optimal state in workload balance. The users’ workload forecasting model is a combination of linear regression and support vector regression methods and offers better performance compared to the conventional reactive self-assessment methods. In the planning phase, a fuzzy self-learning algorithm is used to determine the automated scale of resource provisioning policy. By comparing three criteria of load delay, cost, and amount of consumed energy in the proposed method and recent works, the proposed method has been able to balance all three criteria optimally. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.