A learning-based resource provisioning approach in the fog computing environment

Abstract

ABSTRACT With the recent advancements in distributed computing technologies, the fog computing model has emerged to provide resource capabilities at the edge of the network for executing IoT applications. However, due to the rapid growth of IoT applications and variability their workload over time, achieving an efficient resource provisioning solution to deal with time-varying workloads as one of the challenging tasks in resource management scope to be considered. In this work, we propose a learning-based resource provisioning approach for managing time-varying workloads of IoT applications in the fog network. Our proposed approach utilises the nonlinear autoregressive (NAR) neural network as prediction method and hidden Markov model (HMM) as a decision-maker to identify scaling decisions to provision the fog resources for serving of workloads of IoT applications. The effectiveness of our proposed solution is evaluated using extension experiments under real-world datasets, and the obtained results from iFogSim toolkit demonstrated that it yields a reduction of the delay and cost and improves resource energy consumption compared with existing baseline mechanisms.