Auto-scaling and computation offloading in edge/cloud computing: a fuzzy Q-learning-based approach

The fast growth of under developing mobile applications in recent years has emerged a diversity of delay-sensitive applications such as multimedia streaming, virtual reality, augmented reality, and online gaming applications to facilitate daily activities in different aspects of human life. Edge computing has been raised as an Internet-based distributed computing model to enable mobile devices to offload tasks to nearby edge servers rather than transfer them to remote cloud servers. A joint auto-scaling and task offloading approach in edge/cloud computing is proposed in this paper. Due to dynamic changes in usage and access to mobile applications over time, it requires addressing their workload fluctuations as challenging issues. The future workload is predicted using long short-term memory (LSTM) network, supported with the differential evolution (DE) algorithm for selecting the LSTM hyperparameters. A fuzzy Q-learning technique is also utilized to make scaling decisions at runtime, and a learning automata-based technique is used to make decisions on offloading tasks of mobile devices to edge/cloud layers. The proposed approach is validated using the iFogSim simulator under synthetic and real-world patterns. The results show that it achieves better performance in terms of execution time, energy consumption, and delay violation compared to the baseline approaches.