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

• Published in: Sustainable computing informatics and systems Vol. 36; p. 100787
• Main Authors: Javaheri, Danial, Gorgin, Saeid, Lee, Jeong-A., Masdari, Mohammad
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2022
• Subjects: Energy Efficiency; Fog Computing; Hidden Markov Model; Optimization; Workflow Scheduling; Hidden Markov Model; Energy Efficiency; Optimization; Fog Computing; Workflow Scheduling
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2022.100787

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. •Presenting a multi-fog computing architecture for workflow scheduling in which each IoT network benefits from a broker node that can select a fog computing provider regarding its previous workload.•Proposing DO-HHO, a new discrete and opposition-based version of the HHO algorithm that applies opposition-based learning in the initial population and also in each round of the algorithm, to increase the convergence speed.•Presenting an HMM model for computing the availability probability for each fog environment and selecting the fog computing providers which are more available and are lightly loaded, for submitting the IoT workflows.•Optimizing workflow scheduling in fog computing using the DO-HHO algorithm regarding factors such as makespan and the number of applied VMs.•Conducting an extensive set of experiments and comparing the proposed scheduling scheme with several scheduling schemes.