An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing

Abstract

Workflow scheduling in fog computing is an NP-hard problem that tries to allocate the best possible set of resources for the workflows considering various objectives such as deadlines, costs, energy, and Quality of Service (QoS). However, fog providers may be under heavy loads from the IoT networks, leading to Service Level Agreement (SLA) violations. This article considers an architecture consisting of multiple fog computing providers and provides a Hidden Markov Model (HMM) model for predicting the availability of each fog computing provider regarding factors such as the number of incoming requests to each fog, deadline-missed workflows, and the offloaded tasks from the fogs to the cloud computing. This HMM model is trained using the unsupervised Baum-Welch algorithm, and the availability probability of each fog is computed using the Viterbi algorithm. Then, the fog provider availability probability is used to select a fog computing provider and schedule IoT workflows on it. Additionally, we have improved Harris Hawks Optimization (HHO) algorithm and presented a Discrete Opposition-based of this algorithm, denoted as DO-HHO, for scientific workflow scheduling. The results of extensive experiments conducted using iFogSim demonstrate that our proposed scheme can significantly reduce offloaded tasks on cloud computing, deadline-missed workflows, and SLA violations, outperforming state-of-the-art works.